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\title{Review Article }


\author{Sohini Chowdhury ${ }^{a, b}$, N. Yadaiah ${ }^{a}$, Chander Prakash ${ }^{c, d, *}$,\\
Seeram Ramakrishna ${ }^{e}$, Saurav Dixit ${ }^{f, "}$, Lovi Raj Gupta ${ }^{c}$,\\
Dharam Buddhi ${ }^{g}$\\
a Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli 791109,\\
Arunachal Pradesh, India\\
${ }^{\mathrm{b}}$ Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India\\
c School of Mechanical Engineering, Lovely Professional University, Phagwara, India\\
${ }^{\mathrm{d}}$ Division of Research and Development, Lovely Professional University, Phagwara, India\\
e Department of Mechanical Engineering, National University of Singapore, Singapore\\
${ }^{f}$ Peter the Great St. Petersburg Polytechnic University, 195251, Saint Petersburg, Russian Federation\\
${ }^{g}$ Division of Research \# Innovation, Uttaranchal University, Uttarakhand, 248007, Dehradun, India}
\date{}


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\begin{document}
\maketitle
\section*{Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties \& defects, and numerical modelling}


\section*{A R T I C L E I N F O}
\section*{Article history}
Received 1 December 2021

Accepted 18 July 2022

Available online 11 August 2022

Keywords:

Additive manufacturing

Defects

Feedstock materials

Numerical modeling

Properties

Selective laser melting

\begin{abstract}
A B S T R A C T Additive Manufacturing (AM) has revolutionized the manufacturing industry in several directions. Laser powder bed fusion (LPBF), a powder bed fusion AM process, has been dramatically accepted in various industries due to its versatility with several materials, including alloys. This comprehensive review article primarily explains the basic principle of the LPBF process, scientific and technological progress of several inter-related parameters, feedstock materials, produced properties/defects, and insights of numerical modelling to virtually understand the process behavior. Specific attention has been given to selective laser-meted (LPBFed) properties, driven through the microstructure formations and, thereby, concerning defects. The scope of the post-processing techniques to refine microstructure has also been discussed in this review paper. It has been identified that the defects are vital in LPBF process and are primarily governed by the process parameters. Therefore, a wisely chosen, optimized set of parameters can play a crucial role in minimizing defects considerably. Finally, the numerical modeling discussed in this review paper will help the researchers understand the LPBF process.
\end{abstract}

(C) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (\href{http://creativecommons.org/licenses/by-nc-nd/4.0/}{http://creativecommons.org/licenses/by-nc-nd/4.0/}).
\footnotetext{\begin{itemize}
  \item Corresponding author.
  \item Correspoding author.
\end{itemize}

E-mail addresses: \href{mailto:chander.mechengg@gmail.com}{chander.mechengg@gmail.com} (C. Prakash), \href{mailto:sauravarambol@gmail.com}{sauravarambol@gmail.com} (S. Dixit).
}

\section*{1. Introduction}
Additive Manufacturing (AM), popularly known as 3D printing, has revolutionized the manufacturing industry from developing concepts to producing the final functioning parts. It is also fueling the next generation of innovation and engineering. AM has notably impacted so many industries because it allows the manufacturing of highly complex and thin-walled parts to be cost-effective [1-5]. The continued growth of AM has evoked the requirement of new manufacturing methods, which increased the demand for processing reliability, consisting of applicationspecific optimal designs that are desirable for the intended purpose based on geometry, functionality, and physical properties. The process's design ability usually consists of computer software that allows the designers to predict each component's performance and processing abilities, increasing design efficiency and reduced production cost [6,7]. AM processes prepare three-dimensional (3D) parts by progressively adding thin layers of materials, guided by a computer-aided design (CAD) model. It allows the manufacturing of customized or complex shapes straight from the design or model, without using any expensive tooling such as dies or punches, and eliminates the need for many conventional methods and steps such as tool change [8,9]. Unlike traditional manufacturing methods, intricate parts (such as round holes or straight cuts) can be easily made in one step. Also, a significant number of parts can be reduced by AM processes as there is no need for any assemblies, as parts are made in one go. Citing the benefits mentioned above, the AM is widely used and preferred in aerospace, energy, automotive, and medical industries to design and produce high-performance parts [8]. Compared to conventional methods, the most highlighted advantage of AM processes is its ability to produce complex parts. It shapes directly from the feedstock material by eliminating traditional manufacturing processes like casting, forging, or extrusion [9-14]. Waste minimization is also one of the top benefits of AM due to its near-net shaping capability.\\
Furthermore, the unused feedstock can be reused in AM, making it flexible on feedstock material and cost-effective [15-17]. Few examples of AM-produced parts are shown in Fig. 1.

Currently, the main obstacle of AM is the requirement of a complete understanding of the relation between the processing parameters and the final part properties as well as material properties $[24,25]$. The degree of properties variability is very high, mainly for metallic parts, because of the complex thermal cycles involved in the printing processes. However, there are few metallurgical differences in the properties of AM and conventionally produced parts such as residual stresses, anisotropy, and defects. These characteristics are unique to AM only, which needs to be dealt with for high-performance requirements such as aerospace applications, mainly those parts exposed to high-temperature fatigue [26].

Based on the manufacturing of dense metallic parts, powder bed-based methods like LPBF (laser powder bed fusion), EBM (Electron Beam Machining), and DED (Directed Energy Deposition) are commonly used [10,14,27,]]. All these processes involve the interaction of fed powder with the laser or electron beam that produces the melt pool, leading to rapid melting and solidification [28]. High-temperature gradients and large cooling rates are observed due to the short period of interaction and heat input highly localized in a small region $[29,30]$. These factors also impact the as-built microstructures and lead to high residual stresses [31,32]. Further deterioration of the part's mechanical and fatigue properties will occur due to the inevitable defects $[33,34,35]$. The mechanical properties of the final parts are used to determine whether AM can be accepted in place of traditional methods concerning service quality and durability of the part produced. Hence, it is essential to compare the practical requirements and performance of AM-built and conventionally built parts for quality assessment. Other AM methods include BJG (Binder Jetting) and LENS (Laser Engineered Net Shaping). In LENS, metal powders are injected into a molten pool formed by a highintensity laser beam to produce a part [36].\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-02(1)}\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-02}

Fig. 1 - (a) 3D Ti6Al4V mesh created using EBM [18], (b) Part produced by LPBF [19], (c) Part produced by EBM [20], (d) Lattice structure produced by EBM [21], (e) Ti blade produced by DED [22], (f) porous sample produced by LPBF [23].

In contrast, BJG uses the opposite principle of fusing metallic powders using a binding agent and then exposing it to thermal energy. All the methods have their pros and cons, and the selection shall be made according to the objectives. The type of materials that can be used is one of the differentiators $[37,38]$. Debroy et al. [1] experimented with different materials in all the processes and found that LPBF was the most versatile method of using different materials. And subsequently, LPBF has become an interesting research topic, mainly in the biomedical and aerospace industries [39,40,41,42]. Keeping aside the advantages, there are several challenges in the LPBF methods. The requirement for highly dense parts and high cooling rates are there, which affects the microstructures $[43,44,45,46,47]$. As a result, LPBF-produced parts usually have poor ductility [48]. Other defects such as balling and porosity also affect LPBF process, particularly in fatigue performance. The high cooling rate and temperature gradient lead to residual stress, significantly influencing the crack initiation [49].

This review focuses on the LPBF process and ways to optimize its manufacturability. Many processing parameters influence the LPBF process; therefore, the final part product characteristics can be controlled differently. Specific attention has been given to collect, review, and understand the experimental data obtained in the literature's scientific volume to establish relationships between these input parameters and characteristics of the LPBFed parts. The feedstock materials systems' role in the LPBF process is also discussed in the upcoming sections of this review paper.

\subsection*{1.1. Technology trends, larger built platforms and more lasers}
Laser powder bed fusion (L-PBF) has been rapidly transforming since its development in the 1990s, driven largely by the needs and requirements of industries such as aerospace, biomedical, defense, and automotive [2,11]. This tendency will continue to increase in the future, assisting manufacturers in their transition to more innovative, digital, and high-value manufacturing. The current state of L-PBF technology and its prospects are discussed in this section. The primary emerging themes in L-PBF technology are reviewed, including laser technology breakthroughs, novel metal powders and alloys, post-processing enhancements, and recent automation and quality control [50].

\subsection*{1.1.1. Technological trends to improve the quality}
Among all AM methods that create metals, such as electron beam powder bed fusion, direct energy deposition, binder jetting, and sheet lamination, LPBF has the slowest build rate. Interchangeable feedstock chambers, closed-loop control powder management, automated powder sieving, multi-layer concurrent printing, 2-axis coating, and multi-powder hoppers are among the most recent advances and future directions of LPBF printers. The new enhancements for transferring time and utilizing fast lasers are offered to speed up the procedure. Another advancement in the design of LPBF printers is using lower beam diameter lasers, multi-lasers, uniform inert gas flow, precise positioning systems, high vacuum systems and sensors, and automation to improve part quality [50]. One of the main goals of LPBF machine manufacturers is to improve the quality of the products they create. The new design to provide uniform and consistent inert gas flow, which results in higher fusion stability, is one example of how different machine characteristics affect component quality.

Furthermore, constant gas flow reduces dimensional variations, resulting in products with tighter tolerances. The minimum layer thickness is a direct function of the size of powder particles and the printer's positioning system [51]. Using extremely accurate positioning devices for the build platform reduces the minimum layer thickness, allowing for $10 \mu \mathrm{m}$ accuracy and better resolution for side and lateral surfaces. Closed-loop control (CLC) systems [52] are used as an optional feature in a few modern machines to fine-tune process parameters, improve component quality, and improve the mechanical property and microstructure of the part produced. The capacity to focus intensity inside a small diameter, on the order of $10^{18} \mathrm{~W} / \mathrm{m} 2$, is enabled by ultra-short pulse laser sources. This results in a narrow beam diameter of roughly $30 \mu \mathrm{m}$, allowing for high-resolution processing of precious metals. Furthermore, using multiple lasers simultaneously [53] can reduce recoil pressure in thin walls (by forcing them from two or more sides) and minimize distortion, improving dimensional accuracy.

Even though no preheating is performed, the cooling rate for the first and third layers close to a substrate might drop from 6548 to $2779 \mathrm{~K} / \mathrm{s}$, according to [54]. Preheating minimizes the rapid cooling rate from the base, lowering the risk of deformation and cracks at the sample-to-build tray contact point. Many machines offer preheating options that raise the temperature above $500{ }^{\circ} \mathrm{C}$ to increase build quality and limit the risk of distortion. Another advancement in LPBF machines is the combination of multiple chambers to provide a larger chamber and sample size $[55,56,57]$. Although inert gas in the chamber is required to prevent oxidation, it also has some drawbacks, such as pushing molten, mush, and semi-solid material along the gas flow direction [58,59]. Dimensional aberrations and the risk of distortion rise due to this phenomenon, errors are reduced, and repeatability is improved with increased automation. Instant verification of fusion quality and process stability is feasible because of the combination of cameras and software. Combining part and construction simulation [61] with in-process quality monitoring can provide even more predictability.

\subsection*{1.1.2. Technological trends to reduce production/ manufacturing lead time}
Lower production lead time is one efficient tactic to lower the overall cost of builds. LPBF of metals has a lower build rate than other techniques (laser cladding, electron beam melting, electron beam freeform fabrication, wire arc AM, and plasma arc AM) due to the low layer thickness and hatch space. Various enhancements have recently been implemented to compensate for this problem. Equipment usage rate is increased by using adjustable cylinders and autonomous powder management systems to decrease installation time and increase productivity $[62,63,64]$. Some businesses additionally offer closedloop control powder handling and replaceable cylinders as innovations to increase production speed $[65,66,67,68]$. Even when printing at maximum capacity, circular platforms prevent powder dispersion and do not require any powder filling or

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-04}
\end{center}

Fig. 2 - Laser power comparison of small machines (blue), medium-size machines (red) and heavy machines (green) [51].

unloading throughout the building cycle [69,70,71]. This allows for a consistent construction process, reduced operator time, and increased system security. Another technique to increase the LPBF performance of the machine [62] is to employ a standard automated powder management system [72] over many units in a production line. Several types of machinery use automatic powder sieving and recirculation [73] to cut production time significantly. Human workforce time is reduced, and productivity is increased by automating the screening process and recirculating powder [74,75]. Multi-layer concu- rent printing is the most recent technology for reducing printing time and increasing printing speed by up to 100 times faster than current methods.

Many layers of powder are dispersed on the build surface simultaneously, allowing a laser to scan multiple places at the same time $[76,77,78]$. Introducing tilting re-coater, which decreases powder recoating time, is another innovation that speeds up the process. Low operating expenses can also be achieved by incorporating elements such as a gas-tight design and a gas recycling system $[79,80]$. In modern LPBF machines,

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-05}
\end{center}

\begin{center}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Specifications & SLM 125 & SLM 280 & 2.0 & SLM 280 & SLM 500 & SLM 800 \\
\hline
Build chamber size & $125 \times 125 \times 125 \mathrm{~mm} 3$ & \multicolumn{2}{|l|}{}\begin{tabular}{l}
$280 \times 280 \times$ \\
$365 \mathrm{~mm} 3$ \\
\end{tabular} & $280 \times 280 \times 365 \mathrm{~mm}$ & $500 \times 280 \times 365 \mathrm{~mm} 3$ & \begin{tabular}{l}
$500 \times 280 \times$ \\
$850 \mathrm{~mm}$ \\
\end{tabular} \\
\hline
Number of lasers & 1 & \multicolumn{2}{|l|}{1 or 2} & 1 or 2 & 2 or 4 & 4 \\
\hline
Laser power & $400 \mathrm{~W}$ & \multicolumn{2}{|c|}{}\begin{tabular}{l}
Single $(1 \times 400 \mathrm{~W})$, Twin $(2 \times 400 \mathrm{~W})$, \\
Single $(1 \times 700 \mathrm{~W})$, \\
Twin $(2 \times 700 \mathrm{~W})$, Dual $(1 \times 700$ Wand \\
$1 \times 1000 \mathrm{~W})$ \\
\end{tabular} & \begin{tabular}{l}
Single $(1 \times 400 \mathrm{~W})$, Twin $(2 \times 400 \mathrm{~W})$, \\
Single $(1 \times 700 \mathrm{~W})$, Twin $(2 \times 700 \mathrm{~W})$, Dual \\
$(1 \times 700 \mathrm{~W}$ and $1 \times 1000 \mathrm{~W})$ \\
\end{tabular} & \begin{tabular}{l}
Twin $(2 \times 400 \mathrm{~W})$, Quad $(4 \times 400 \mathrm{~W})$ \\
Twin $(2 \times 700 \mathrm{~W})$, Quad $(4 \times 700 \mathrm{~W})$ \\
\end{tabular} & \begin{tabular}{l}
Quad $(4 \times 400 \mathrm{~W})$ \\
Quad $(4 \times 700 \mathrm{~W})$ \\
\end{tabular} \\
\hline
Laser type & IPG fibre laser & \multicolumn{2}{|l|}{IPG fibre laser} & IPG chamber laser & IPG fibre laser & IPG fibre laser \\
\hline
Build rate & Up to $25 \mathrm{~cm} 3 / \mathrm{h}$ & \multicolumn{2}{|l|}{Up to $88 \mathrm{~cm} 3 / \mathrm{h}$} & Up to $88 \mathrm{~cm} 3 / \mathrm{h}$ & Up to $171 \mathrm{~cm} 3 / \mathrm{h}$ & Up to $171 \mathrm{~cm} 3 / \mathrm{h}$ \\
\hline
Variable layer thickness & \begin{tabular}{l}
$20-75 \mu \mathrm{m}, 1-\mu \mathrm{m}$ \\
increments \\
\end{tabular} & \multicolumn{2}{|l|}{$20-75 \mu \mathrm{m}$} & $20-90 \mu \mathrm{m}$ & $20-75 \mu \mathrm{m}$ & $20-90 \mu \mathrm{m}$ \\
\hline
Min. feature size & $140 \mu \mathrm{m}$ & \multicolumn{2}{|l|}{$150 \mu \mathrm{m}$} & $150 \mu \mathrm{m}$ & $150 \mu \mathrm{m}$ & $150 \mu \mathrm{m}$ \\
\hline
Beam diameter & $70-100 \mu \mathrm{m}$ & \multicolumn{2}{|l|}{$80-115 \mu \mathrm{m}$} & $80-115 \mu \mathrm{m}$ & $80-115 \mu \mathrm{m}$ & $80-115 \mu \mathrm{m}$ \\
\hline
Max. scan speed & $10 \mathrm{~m} / \mathrm{s}$ & \multicolumn{2}{|l|}{$10 \mathrm{~m} / \mathrm{s}$} & $10 \mathrm{~m} / \mathrm{s}$ & $10 \mathrm{~m} / \mathrm{s}$ & $10 \mathrm{~m} / \mathrm{s}$ \\
\hline
\begin{tabular}{l}
Machine dimensions \\
$(\mathrm{L} \times \mathrm{W} \times \mathrm{H}$ \\
\end{tabular} & \begin{tabular}{l}
$1400 \mathrm{~mm} \times 900 \mathrm{~mm} \times$ \\
$2460 \mathrm{~mm}$ \\
\end{tabular} & \multicolumn{2}{|l|}{}\begin{tabular}{l}
$2600 \mathrm{~mm} \times 1200 \mathrm{~mm} \times$ \\
$2700 \mathrm{~mm}$ \\
\end{tabular} & \begin{tabular}{l}
$2900 \mathrm{~mm} \times 1200 \mathrm{~mm} \times$ \\
$2500 \mathrm{~mm}$ \\
\end{tabular} & \begin{tabular}{l}
$5200 \mathrm{~mm} \times 2800 \mathrm{~mm} \times$ \\
$2700 \mathrm{~mm}$ \\
\end{tabular} & Depending on set up \\
\hline
Weight & $750 \mathrm{~kg}$ & \multicolumn{2}{|c|}{\multirow{2}{*}}{}\{\begin{tabular}{l}
$1300 \mathrm{~kg}$ \\
NA \\
\end{tabular}\} & $1300 \mathrm{~kg}$ & $2400 \mathrm{~kg}$ & NA \\
\hline
\multirow[t]{3}{*}{Price} & $€ 400,000-€ 500,000$ &  &  & $€ 190,500.00$ & $€ 1-2 \mathrm{M}$ & NA \\
\hline
 &  &  & SLM 125 & SLM 2802.0 &  &  \\
\hline
 &  &  & SLM 500 & SLM 800 &  &  \\
\hline
\end{tabular}
\end{center}

Table 3 - The latest GF LPBF machine models and specifications [84].

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-07(1)}
\end{center}

Specifications

Number of lasers

Laser power type Laser Type

Layer thickness, range, pre-set

Build chamber size

Repeatability

Minimum feature size

Typical accuracy

Dimensions $(\mathrm{w} \times \mathrm{d} \times \mathrm{h}$ )

Material loading

DMP Factory 500 Printer

3

$500 \mathrm{~W}$

Fibre laser

Adjustable, min. $2 \mu \mathrm{m}$, max. $200 \mu \mathrm{m}$, typ. 30-60-90 $\mu \mathrm{m}$

$500 \times 500 \times 500 \mathrm{~mm}(20 \times 20 \times 20 \mathrm{in})$

x y z $20 \mu \mathrm{m}$ (0.00079 in)

$100 \mu \mathrm{m}$ (0.0039 in)

$\pm 0.1-0.2 \%$ with $\pm 50 \mu \mathrm{m}$ minimum

$3010 \times 2290 \times 2820 \mathrm{~mm}(118 \times 90 \times 111 \mathrm{in})$

Manual or semi-automatic

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-07}
\end{center}

an ultra-fast Quad laser $[81,82,83]$ with a processing speed of up to $10 \mathrm{~m} / \mathrm{s}$ and high powers of over $1000 \mathrm{~W}$ enhances the build pace. Fig. 2 depicts the laser power of several machines. As can be observed, recent selective laser melting (SLM) systems (SLM 800 and SLM 500), which are designated as industrial machines, utilize lasers with a power of $2800 \mathrm{~W}(4700 \mathrm{~W})$, allowing for faster printing of larger components. With a comparable industrial categorization, GE machines (General Electric (GE) Additive Project A.T.L.A.S.) are also equipped with a high-power laser that can output up to $1500 \mathrm{~W}$ for faster printing, as shown in Tables 1-4.

Fig. 3 shows the NXG XII 600, a new SLM $®$ machine from SLM Solutions Group AG in Lübeck, Germany, has been officially launched and is now ready for commercial use [85]. The Laser beam powder bed fusion (PBF-LB) Additive Manufacturing machine has twelve $1 \mathrm{~kW}$ lasers and a $600 \times 600 \times 600 \mathrm{~mm}$ square build environment. The NXG XII 600 , according to the makers, is the fastest AM machine on the market, with build speeds twenty times quicker than a singlelaser machine and technical features including a zoom capability to ensure maximum productivity and dependability. It is intended for use in serial manufacturing for high-volume applications and the construction of massive parts, opening up new possibilities in the automotive and aerospace industries and paving the way for industrialized serial AM.

\subsection*{1.2. Laser beam profiles}
In the LPBF process, during the laser-powder interaction, the distribution of heat intensity throughout the powder bed is considered to follow a Gaussian beam pattern in most circumstances. However, heat distribution throughout the surface is a complex process influenced by several elements such as beam quality and laser wavelength. It must be considered when presenting the laser-material interaction in a way that accurately portrays the actual beam. Soylemez [86] used a Gaussian laser beam model to simulate the temperature profile with the Gaussian beam model for LPBF processing\\
Ti-6Al-4V alloy. Fig. 4 has been illustrated, as has the laser absorption efficiency value; it shows the melt pool crosssection images from the simulation. In the simulation, the red region represents the molten area, while the black dashed line represents the melt pool border result transferred from an optical microscope image of the experimental sample crosssection. Fig. 4(a) and 4(b) show the results for $100 \mathrm{~m}$ and $260 \mathrm{~m}$ beam diameters, respectively. Due to the software's post-processing procedure, the contour plot of the melt pool portion has a discontinuity at the boundary.

Ahsan and Ladani [29] used a non-Gaussian laser beam model to simulate the temperature profile, bead geometry, and elemental evaporation. They compared the results with the Gaussian beam model for LPBF processing Inconel 718 alloy. The comparison of thermal characteristics of nonGaussian and Gaussian beams has been performed. The Gaussian beam produces a more localized temperature profile, resulting in a more significant temperature build-up at the melt pool's center. With the inclusion of a beam quality factor, which lessens the beam focus, a more uniform distribution of heat away from the center of the beam is noticed. With a lower maximum temperature than the Gaussian beam, the profile obtained with the non-gaussian beam was more comparable to the observed temperature profile. The camera's failure to measure temperature in the lower temperature range causes the experimental data to spike in the lower temperature range. This specific literature could not provide the laser profile. However, because the temperature profile of the non-Gaussian beam was comparable to the experimental result, the laser profiles should also be similar. The Gaussian model for all three combinations has a higher temperature and intensity in focus because of the high concentration. Shi et al. [87] used the Gaussian (circular) and elliptical (transverse and longitudinal) laser beam shapes for the LPBF process for $316 \mathrm{~L}$ stainless steel. It has been seen that a wide beam width produced by an elliptical transverse laser beam correlates with the likelihood of producing equiaxed grains through nucleation processes. At the start of a track, columnar

Table 4 - The latest Additive Industries LPBF machine models and specifications [55].

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-08(1)}
\end{center}

development is challenging to avoid. The growth morphology in the absence of heat input is determined by the melt pool width and depth achieved, as well as the degree of thermal undercooling. According to the researchers, this fundamental understanding of the physics of local beam shaping for microstructural control should have ramifications for future complicated beam shape designs and beam modulation. Iveković et al. [88] studied microstructural and crack mitigation in LPBF processing of Hastelloy-X using an experimental and numerical approach. The LPBF process was simulated by considering two different laser beam profiles, Gaussian and top-hat. To understand the fundamental understanding of physics, the effect of laser beam intensity distribution and melt track overlap on temperature progression, solidification behavior, and crack creation were studied. The Gaussiandistributed beam causes keyhole melting and sharp cooling rate; however, the top-hat type beam causes wide and shallow conduction mode melt pools, a slower cooling rate, and a

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-08}
\end{center}

Fig. 3 - New SLM machine equipped with 12 lasers with $1 \mathrm{~kW}$ (NXG XII 600) [85]. coarser sub-grain microstructure. Okunkova et al. [89] reported that top-hat and donut-shaped profiles reduced spatters' formation and denudation zone width. Wischeropp et al. [90] said that the donut-shaped beam profile is stable and productive. A large melt-pool, fewer defects (cracks, balling, and porosity), and hence better process resilience has been observed.

\section*{2. LPBF: A prominent AM process}
LPBF is one of the AM processes that have revolutionized the manufacturing industry as it allows manufacturing complex components to reduce cost, time, and labor. It enables manufacturing complex shapes without tooling, castings, or conventional manufacturing methods. The LPBF process is essential in understanding the relationship between operating parameters and the final part properties. LPBF is one of the powder bed fusion methods where a powder bed is spread, and pre-determined regions are exposed to high-intensity laser energy. That way, powders can be melted and fused layer-bylayer in compliance with the design prepared in the CAD software. The name itself tells a lot about the process. The term 'laser' represents that a laser energy heat source is employed in the process, 'melting' denotes that the powder is being melted and 'selective' means that only selective parts of the powder bed are under the effect of the heat source [91]. The LPBF system layout usually includes a laser source, a building platform, an automatic system to deliver powder, a controlling system, and complementary parts such as rollers, scrappers, etc. [92]. The movement and the focus of the high-intensity laser beam is monitored by a beam deflection system which includes Galvano mirrors and flat field focusing lens. Overall, the stages\\
(a) beam diameter $=100 \mu \mathrm{m}$

(b) beam diameter $=260 \mu \mathrm{m}$\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-09}

Fig. 4 - Illustrated melt pool comparison between the simulation and the experiment at $P=370 \mathrm{~W}$ and

\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-09(2)}\\
simulation, and the dashed line is the melt pool boundary of the experimental cross-section for the same process parameters. (a) $\mathrm{D}=100 \mu \mathrm{m}$, (b) $\mathrm{D}=260 \mu \mathrm{m}$ [86].

in producing a part via LPBF can be categorized as such: (i) Designing and modelling of the 3D part to be produced in a CAD software and then slicing the model in the required number of layers with a defined layer thickness; (ii) For the fabrication, a substrate is fixed on the build platform. This is the base level upon which layers will be deposited; (iii) The build chamber is moved into a protective atmosphere, mostly of nitrogen and argon, to minimize the risk of surface oxidation; (iv) According to the pre-defined layer thickness, the first layer is spread on the build platform; (v) The laser then scans the powder bed in the pre-defined path to fabricate the layer wise shape as commanded by the CAD software and the model designed; (vi) Lowering of the building platform and repeating the last two steps of spreading the powder bed and scanning it multiple times until the finished part is produced. The components of a LPBF machine is given in Fig. 5.

In LPBF, the powder particles are fully melted by the heat supplied in the laser beam's form into welding beads, like the principle of a welding process. Hence, it is a type of deposition welding process [93]. Several physical behaviors are happening in a LPBF process: reflection, phase transformation, absorption, heat transfer, solidification, chemical reactions, transportation of molten metal, or the flow of molten metal within the molten pool $[93,94]$. The melt pool is formed due to the conversion of light energy into heat energy, and eventually, due to surface tension, the melt pool adopts the shape of a circular or segmented cylinder [95]. Transient temperature filed with temperature as high as $105^{\circ} \mathrm{C}$ are formed due to the very short interaction time period between the powder bed and laser, and subsequently, a rapid quenching effect also takes place with cooling rates as high as $106-108^{\circ} \mathrm{C} / \mathrm{s}$ [96]. The rapid solidification results are building nonequilibrium metallurgical phenomena like refining microstructures, solid solution hardening, and generation of metastable phases that could improve the mechanical properties of the produced part [97,98].

The main aim of the LPBF method is to fabricate fully dense parts. The difficulty in obtaining such a result is that there is

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-09(1)}
\end{center}

Fig. 5 - Schematic of a typical LPBF machine.

an absence of any mechanical pressure in the LPBF process, and the fluid dynamics are also governed by only gravity and capillary forces. The lack of mechanical pressure results in the solubility of elements during solidification, which causes discontinuous melting of tracks and the generation of poor and uneven surfaces $[99,100]$. The high degree of variation of thermal fluctuation experienced by the materials during the LPBF process increases residual stresses in layers going through rapid solidification [101]. Heat-affected zones (HAZ) take birth around the melt pool due to a very high heating and cooling rate. This HAZ can alter the microstructure and composition of the material, which governs the quality of the produced part [91]. The processing parameters can control the thermal behavior at any instant in time. These are the hatch spacing, layer thickness, scanning speed, laser power and scanning strategy. Fig. 6 shows the process parameters in LPBF process. The chosen parameters allow complete melting of powder and complete fusion with the preceding layer. Inappropriate selection of these parameters may lead to unwanted effects like thermal cracks, balling, and porosity or indicate other undesirable effects. Hence, it is essential to establish a relation between these parameters and output results to optimize the processing parameters to achieve the desired result.

\section*{3. Materials}
This section presents recent efforts and advancements made in processing functional materials using the additive manufacturing process to develop industrial products. Table 5 shows the types of available materials processed via additive manufacturing.

\subsection*{3.1. Titanium-based alloys}
Titanium (Ti) alloys are highly employed in various biomedical, automotive, and aerospace industries due to their high strength-to-weight ratio, compatibility, and high-temperature resistance [102,103]. Generally, the materials used in the

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-10}
\end{center}

Fig. 6 - Schematic of LPBF processing parameters.

biomedical sectors should fulfill the essential requirements of low Young's modulus, high strength, low density, high wear resistance, high corrosion resistance, and high compatibility. Ti alloys possess excellent properties mentioned above and prove to be a good choice for the biomedical industry $[40,43,104,105,106]$. Their biomedical applications include dental fields, joint replacements, implants, orthodontic parts, surgical instruments, and artificial heart valves. Due to high time, energy, and material requirement, manufacturing has always been a problem for Ti alloys. Still, the advent of additive manufacturing, especially LPBF, has led to more manufacturing of Ti parts $[107,108,109,110]$. Commercially pure (CP) Ti is now replaced by Ti-6Al-4V as an orthopedic prosthesis because of its excellent mechanical characteristics $[111,112,113]$. Other Ti alloys are also considered substitutes for $\mathrm{CP} \mathrm{Ti}$ and Ti-6Al-4V because of their non-toxic properties [114,115,116]. Ti-13Nb-13Zr is developed by alloying niobium $(\mathrm{Nb})$ and zirconium (Zr), and is emerging as an excellent allow with excellent properties. This alloy can readily replace $\mathrm{Ti}-6 \mathrm{Al}-4 \mathrm{~V}$. Nb's presence suppresses the alpha phase formation [117,118,119]. Many research papers have already established solid grounds for $\mathrm{Ti}-13 \mathrm{Nb}-13 \mathrm{Zr}$ to replace Ti-6Al-4V [120,121,122,123,124,125,126,127,128]. Several investigators also examined the LPBF lattice structures for mechanical performances $[129,130,131]$ and porous biomaterials $[132,133,134]$.

\subsection*{3.1.1. LPBF of $\alpha$-Ti based material}
Optimization of the process parameters greatly influences the production part's mechanical properties, densification, and microstructural behavior. It has been validated that an energy density (E) of $120 \mathrm{Jmm}-3$ is sufficient to fabricate almost entirely dense CP-Ti parts. However, other processing parameters like scanning speed and laser power must be well optimized to achieve the highest possible density (Table 6). It can be observed that the relative density increases with an increase in laser power at constant power density. However, the change is not uniform.

Vracken et al. [48] stated that the processing parameters ultimately influence microstructure development in LPBF process. The microstructural behavior of CP-Ti fabricated by LPBF varies from plate-like $\alpha$ to acicular martensitic $\alpha^{\prime}$ phase, entirely governed by the processing parameters (Fig. 7) [106]. It has been observed that scanning speed plays a vital role in differentiating the microstructure. When scanning speed is below $100 \mathrm{~mm} / \mathrm{s}$, for the energy density of $120 \mathrm{~J} / \mathrm{mm}^{3}$, the full transformation of the $\beta$-phase to $\alpha$ phase occurs during solidification due to energy thermalization within the melt pool (Fig. 7a). But when scanning speed is more than $100 \mathrm{~mm} / \mathrm{s}$, for the same energy density, the formation of $\alpha^{\prime}$ microstructure occurs due to the increase in thermal and kinetic undercooling (Fig. 7b). A widely observed LPBF microstructure produced by Ti $-6 \mathrm{Al}-4 \mathrm{~V}$ is the $\alpha^{\prime}$ martensite in columnar prior $\beta$ grains $[129,130,131,132,133]$. Such microstructure formation is due to the processing parameters selection, which could result in a cooling rate of more than $410 \mathrm{~K} / \mathrm{s}$ [134] beyond the starting martensite temperature, which promotes $\alpha^{\prime}$ martensite growth. Heat conduction leads to the elongation of $\beta$ grain in the build direction [135]. It has been proven that LPBF-produced CP-Ti parts have better mechanical properties than those manufactured by conventional processes $[106,137,138,139]$. The superior mechanical properties might result from grain refinement in the LPBF process, as presented in (Table 7).

\subsection*{3.1.2. LPBF of $\beta$-type Ti-based material}
One of the best examples of $\beta$-Ti alloy, which shows excellent properties like high strength and low modulus, is $\mathrm{Ti}-24 \mathrm{Nb}-4 \mathrm{Zr}-8 \mathrm{Sn}$, commonly known as Ti2448. This alloy also needs optimized processing parameters to develop fully dense parts [140]. The increasing scan speed shows a gradual decrease in hardness and density (Table 8). Hence, it can be said that microhardness and density are very much dependent on the processing parameters. Two sets of densities, almost dense (99.3\%) and intermediate dense (98.2\%), of LPBF, produced parts at two different scan speeds can be observed in Fig. 8. The dark bands in the figure are nothing but laser tracks. Table 9 compares the properties of Ti2448 parts manufactured by different methods, showing that the ultimate strength and yield strength of parts produced by rolling and forging are slightly higher than those produced by LPBF [140].

\subsection*{3.1.3. LPBF of $(\alpha+\beta)$ Ti based material}
A microstructure of lamellar $(\alpha+\beta)$ can help enhance the ductility of Ti-6Al-4V without compromising the material's yield strength [141]. Lamellar $(\alpha+\beta)$ can be transformed to $\alpha^{\prime}$ martensite by altering the energy density and utilizing the cyclic reheating associated with layer deposition $[45,142,143]$. LPBF studies on $(\alpha+\beta)$ Ti has mostly been done on Ti-6Al-4V $[144,145]$ and $\mathrm{Ti}-6 \mathrm{Al}-7 \mathrm{Nb}$ [146,147]. Because of the large temperature gradients during the LPBF process, the SEM microstructure of LPBF-produced Ti-6Al-4V parts shows fine acicular martensite $\mathrm{CP}-\mathrm{Ti}$ and the process parameters need to be optimized for obtaining the highest possible dense parts. Table 10 gives that LPBF-produced parts have superior characteristics to those produced by other manufacturing methods. This is attributed due to the formation of martensite microstructure during the LPBF process [144]. Ti-6Al-7Nb is one other Ti-based alloy used for biomedical implants and has a favorable character of higher resistance to corrosion and better mechanical properties [148,149] compared to $\mathrm{Ti}-6 \mathrm{Al}-4 \mathrm{~V}$. Both Ti-6Al-7Nb and Ti-6Al-4V produced by

\section*{Table 5 - Different type of materials processed by the additive manufacturing process for various industrial applications}
$[40,43,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126]$.

Type of material Composition Applications Key benefits

Metal and Alloys Aluminum alloys Aerospace, Automobile

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-11(1)}
\end{center}

Aeronautic, aerospace and biomedical Application

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-11(2)}
\end{center}

Titanium alloy

Biomedical Implants and Aerospace components

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-11}
\end{center}

Highest tensile strength and Hardness

Lightweight and high hardness

High strength, light weight and excellent bio-mechanical properties

Ni-based superalloys

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-12(1)}
\end{center}

Impeller;

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-12(2)}
\end{center}

High yield strength with high operating temperatures

Corrosion resistant; Bio-compatibility

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-12}
\end{center}

Shape memory;\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-13}

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-14}
\end{center}

Table 6 - Respective laser power and relative density for LPBF produce GP-Ti with fixed energy density as $120 \mathrm{~J} /$ $\mathrm{mm}^{3}$.

Energy Density Laser power (W) Relative density (\%) $\left(\mathrm{J} / \mathrm{mm}^{3}\right.$ )

\begin{center}
\begin{tabular}{lcc}
\hline
120 & 84.70 & 96.43 \\
120 & 89.78 & 97.40 \\
120 & 109.9 & 98.2 \\
120 & 125.32 & 98.51 \\
120 & 134.92 & 98.6 \\
120 & 149.96 & 99.21 \\
120 & 165.20 & 99.48 \\
120 & 170.28 & 99.33 \\
120 & 175.17 & 99.21 \\
120 & 180.2 & 99.17 \\
\hline
\end{tabular}
\end{center}

LPBF have similar $\alpha^{\prime}$ martensite microstructure $[150,151,152,153,154,155,156]$. LPBF-produced Ti-6Al-7Nb parts exhibit superior properties than those obtained by casting (Table 11). Al alloys are one of the most popular material systems in SLM research, as they are used in numerous high-value applications. However, processing them is tough due to the problems of laser-melting aluminum, resulting in parts with various flaws. In recent years, several researchers have devised techniques to address these, reporting effective\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-15}

Fig. 7 - Microstructure of LPBF produced CP-TI part. (a) $\alpha$ grains and (b) $\alpha^{\prime}$ grains [106].\\
SLM of various Al-alloys and exploring its potential application in advanced componentry [157].

\subsection*{3.1.4. LPBF of composite Ti material}
Any research on LPBF and Ti alloys is mainly restricted to some popular alloys. But to increase the wear resistance, yield strength, and ultimate strength, ceramics such as SiC, TiC or $\mathrm{TiB}_{2}$ are added to $\mathrm{Ti}[158,159]$. Adding titanium monobromide (TiB) as a reinforcement provides chemical, thermodynamic, and mechanical stability can be seen in Fig. 9. An in-situ reaction between $\mathrm{Ti}$ and titanium dibromide $\left(\mathrm{TiB}_{2}\right)$ leads to Ti-TiB composite creation [160,161]. Attar et al. [162] produced the highest dense Ti-TiB part by optimizing the process parameters and using $\mathrm{Ti}-5 \mathrm{wt} \% \mathrm{TiB}_{2}$ mixture powder. It was observed that needle-shaped morphology was distributed all over the Ti matrix. Table 10 compares the properties of LPBF fabricated Ti composites with those produced by different methods. It can be seen that the increase of microhardness of Ti-TiB can be due to the hardening effect due to the refinement of Ti grains. And the superior yield and ultimate strength of Ti-TiB are due to the strengthening effect caused by TiB particles.

\subsection*{3.1.5. LPBF of porous Ti material}
The primary purpose of porous Ti material is to replicate the natural bone in the biomedical industry. The CP-Ti and $\mathrm{Ti}-6 \mathrm{Al}-4 \mathrm{~V}$ are the main center of attention when LPBF work is carried out for porous Ti material. $55-75 \%$ porous Ti structure was fabricated by LPBF, which is analogous to the natural human bone, and tested by compressive testing [163]. Attar et al. [164] successfully fabricated porous Ti-TiB and CP-Ti materials by LPBF with porosity levels varying as $10 \%, 17 \%$ and $37 \%$. These materials' elastic modulus and yield strength were close enough to human bones, promoting that they can be used as a substitute for implants. The major objective to utilize the LPBF to develop high strength and biocompatible porous Ti-alloy based implant that promote osseointegration in the host body $[165,166]$.

\subsection*{3.2. Magnesium-based alloys}
Magnesium (Mg) is the sixth most available element on earth's crust [167]. Magnesium-based alloys are preferred for weightsensitive applications because of their light structural weight, lighter than other $\mathrm{Al}$ or Ti elements [168]. Because of its lightweight and high-strength properties, $\mathrm{Mg}$ is getting attention from different industries such as aviation and automobiles $[169,170]$. The Mg-based alloy also shows excellent mechanical properties, castability and machinability, high thermal stability, and high thermal and electrical conductivity [171,172,173,174,175]. But the problem with $\mathrm{Mg}$ is its low corrosion resistance and other negative properties such as low elastic modulus, low strength, and poor creep resistance $[176,177]$. Mg is still not employed fully in the clinical field due to its poor formability, rapid degradation, and hydrogen evolution [178]. Therefore, researchers continuously develop new $\mathrm{Mg}$ alloys and composites to fulfill specific job requirements. One of the $\mathrm{Mg}$ alloys, $\mathrm{Mg}-\mathrm{Zn}$, is the top alloy due to the combined presence of high strength and corrosion resistance [179,180,181]. Most of the Mg-Zn products are produced by

Table 7 - Tensile properties, Hardness properties and Compressive properties of CP-Ti fabricated by different methods [139].

Tensile Properties

\begin{center}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{Processing method} & $\sigma_{0.2} / \mathrm{MPa}$ & \multicolumn{2}{|r|}{$\sigma_{\mathrm{UTS}} / \mathrm{MPa}$} & $\varepsilon_{\mathrm{f}} / \%$ \\
\hline
\multicolumn{2}{|l|}{LPBF} & 555 & \multicolumn{2}{|r|}{757} & 19.5 \\
\hline
\multicolumn{2}{|l|}{LPBF} & 500 & \multicolumn{2}{|r|}{650} & 17 \\
\hline
\multicolumn{2}{|l|}{Sheet Forming} & 280 & \multicolumn{2}{|r|}{345} & 20 \\
\hline
\multicolumn{2}{|l|}{Full Annealed} & 432 & \multicolumn{2}{|r|}{561} & 14.7 \\
\hline
\multicolumn{3}{|c|}{Hardness Properties} & \multicolumn{3}{|c|}{Compressive Properties} \\
\hline
Condition & \multicolumn{5}{|c|}{Vickers Hardness/VH Condition $\sigma_{\mathrm{UCS}} / \mathrm{MPa} \varepsilon_{\max } / \%$} \\
\hline
LPBF & 261 &  & LPBF & 1136 & 51 \\
\hline
Casting & 210 &  & CG-Ti & 820 & 60 \\
\hline
55\% Cold Rolled 2 & 268 &  & UFG-1 - - - - & 900 & 35 \\
\hline
\end{tabular}
\end{center}

Note: $\sigma_{\text {UTS }}$ is the ultimate tensile strength, $\sigma_{\text {UCS }}$ is the ultimate compressive strength, $\varepsilon_{\mathrm{f}}$ is the fracture tensile strain, $\sigma_{0.2}$ is the yield strength, $\varepsilon_{\max }$ is the maximum strain, CG-Ti is the coarse grained Ti and UFG-Ti is ultrafine grained Ti.

permanent mold casting [182], extrusion [183], rolling [184], and pressing [185]. But due to the closed-packed hexagonal structure (HCP) of the $\alpha \mathrm{Mg}$ matrix, its formability is relatively poor [186]. Hence, such alloys' plastic working must be conducted at elevated temperatures surrounding, increasing the manufacturing cost. The introduction of AM, mainly LPBF, has enabled the rapid production of such alloys with high-density products without using any moulds of fixtures [10].

\subsection*{3.2.1. LPBF processing window of $\mathrm{Mg}$ and $\mathrm{Mg}$-based alloys}
$\mathrm{Ng}$ et al. [187] stated the potential to employ Mg powders in the LPBF process as they successfully melted Mg tracks completely in an inert atmosphere. The processing window for the single-track Mg powders was established based on the interaction between the laser source and the Mg powder under different sets of processing parameters [188,189]. Several researchers have also tried to establish the processing window for Mg and its alloys, such as AZ91D, WE43, ZK60, and $\mathrm{Mg}-9 \% \mathrm{Al}$ based on their formability to manufacture singlelayered and multilayers three-dimensional objects [190]. Fig 10 shows a map of processing windows for $\mathrm{Mg}$ alloy $\mathrm{Mg}-9 \%$ Al. The laser parameter, which is laser energy density, is considered a single parameter to influence the occurrence of the regions and microstructures and compare the processing conditions for single and multiple layer parts [191].

Table 8 - Decrease in Relative density and hardness on increase of scanning speed of LPBF produced Ti2448 at laser power of $200 \mathrm{~W}$.

\begin{center}
\begin{tabular}{lcc}
\begin{tabular}{l}
Laser scan \\
speed $(\mathrm{mm} / \mathrm{s})$ \\
\end{tabular} & \begin{tabular}{c}
Relative \\
density (\%) \\
\end{tabular} & \begin{tabular}{c}
Hardness \\
$(\mathrm{Hv})$ \\
\end{tabular} \\
\hline
225 & 96.94 & 214.20 \\
300 & 96.90 & 242.45 \\
400 & 96.24 & 241.22 \\
500 & 95.61 & 237.54 \\
600 & 95.35 & 238.77 \\
700 & 91.04 & 226.48 \\
800 & 87.23 & 221.57 \\
900 & 82.744 & 193.80 \\
\hline
\end{tabular}
\end{center}

Based on the quality of produced parts through LPBF, four regions can be categorized for the behavior of $\mathrm{Mg}$ powder and its alloys concerning varying scan speeds and laser powers. They are explained below.

\begin{enumerate}
  \item In this region, the energy density and laser power are too high for any range of scanning speed. As a result of this high temperature, there is evaporation and ionization of $\mathrm{Mg}$ powder in the melt pool because of low melting point of $\mathrm{Mg}$. The evaporated powders expand and create a massive recoil effect in the melt pool, which blows away the liquid and the powder to result in no track formation [191]. The high temperature also affects the viscosity of the overheated liquid $\mathrm{Mg}$, which results in melt pool instability. The high energy input also gives birth to high thermal stresses, leading to the deformation of parts [192].

  \item In this region, the energy density and laser power are too low for any range of scanning speed. Using a very low energy input and high scanning speed would not give an appropriate interaction time resulting in partial melting of $\mathrm{Mg}$ powders. The low laser energy is insufficient to give rise to the liquid phase, and consequently, there is a poor bond neck between the particles. In such cases, fusion among the particles leads to parts with no mechanical strength and a great number of un-melted debris are found on the surface [193]. A heat-affected zone (HAZ) is formed due to partial melting as heat is conducted away from the melt pool center to the surrounding powders [188]. If the scanning speed is even faster, the low density and chemical characteristic of the Mg powders will lead to oxidation and formation of $\mathrm{MgO}$ that is the black fog which would disturb the process environment [194]

  \item In this zone, a good amount of melting can be accepted of $\mathrm{Mg}$ powders, with a melt pool that is relatively more stable and yields tracks with good bonding among the particles. The range of laser energy in this zone is very favorable to increase the powder bed's temperature while diminishing the melt pool's viscosity such that the melt can spread evenly over the preprocessed layer to obtain a denser part. Under low laser power and high scanning speed, a large low energy input causes surface melting of particles, resulting in a weak bond neck between the particulates. Under the laser conditions, the produced samples showed a powder stacking structure with no mechanical strength, as shown in zone [190]. LPBF in this region has proved to produce parts with good properties when experimented with CP-Ti [146] and $\mathrm{Ti}-\mathrm{TiB}_{2}$ components [162].

  \item This one is characterized by the occurring of the balling region. Balling is an agglomeration of ball-shaped melted powders to form large melt pools. This happens mainly due to low laser energy density input, which is nothing but low power, high layer thickness, and high scanning speed [102]. Since the balling phenomena tends to occur in this zone, it could deteriorate the surface of the produced part.

\end{enumerate}

\subsection*{3.3. Aluminium-based alloys}
Aluminium (Al) and its alloys are the mostly employed materials for any structures after steel and iron, and due to their excellent properties of high strength, low density and good\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-17}

Fig. 8 - Microstructure of Ti2448 produced by LPBF at two different scanning speed and with relative densities of (a) $99.3 \%$ and (b) $98.2 \%$ [140].

corrosion resistance, they are applicable in various industries such as weapons, power electronics, automobiles and aviation $[195,196,197]$. Traditional manufacturing methods such as extrusion, casting and forging are commonly employed to manufacture any part from $\mathrm{Al}$ and its alloys [198]. Though such methods have proven to be effective manufacturing methods for $\mathrm{Al}$, there are still many drawbacks associated with it. In casting, coarse microstructures are formed due to low cooling rates and many defects such as porosity, slag inclusion, and offset defects $[199,200,201]$. The separated manufacturing line for high-performance $\mathrm{Al}$ alloys limits the flexibility of the manufacturing process. Due to the advancement in modern industries, the performance requirements for

Table 9 - Properties of Ti2448 samples produced by different methods.

\begin{center}
\begin{tabular}{lcccc}
Processing method & $\mathrm{E} / \mathrm{GPa}$ & $\sigma_{0.2} / \mathrm{MPa}$ & $\sigma_{\mathrm{UTS}} / \mathrm{MPa}$ & $\varepsilon_{\mathrm{f}} / \%$ \\
\hline
LPBF & 53 & 563 & 665 & 13.8 \\
Hot Rolling & 46 & 700 & 830 & 15 \\
Hot Forging & 55 & 570 & 765 & 13 \\
\hline
\end{tabular}
\end{center}

Note: $\sigma_{0.2}$ is the yield strength, $\varepsilon_{\mathrm{f}}$ is the fracture tensile strain, $\sigma_{\mathrm{UTS}}$ is the ultimate tensile strength and $\mathrm{E}$ is the young's modulus. $\mathrm{Al}$ parts are at the top. Hence, structures are needed to fulfil such requirements and be manufactured to reduce cost and time $[202,203,204]$

LPBF is commonly known to be giving good results of fully dense parts in a single scan itself. But for some alloys such as $\mathrm{Al} / \mathrm{Fe}_{2} \mathrm{O}_{3}$ powders, it was necessary to employ in situ formation of particle reinforced $\mathrm{Al}$ matrix to overcome defects such as balling, doss formation and part distortion, which causes poor surface finish of the part produced. The in-situ reaction, which is largely affected by the concentration of $\mathrm{Fe}_{2} \mathrm{O}_{3}$ additives, dictates the range of the suitable processing parameters. Furthermore, it is also affirmed that the process parameters have a great role in influencing the microstructure, fracture behaviour and high cycle fatigue for Alloys such as AlSi10Mg fabricated through LPBF [205]. In the coming future, the manufacturing methods for $\mathrm{Al}$ alloys with diverse design, high accuracy and near net shape structure could be of great interest in the manufacturing industry. The exposure of Al to the LPBF process has sorted out many of the problems attached to the traditional manufacturing processes [206,207,208]. Louvis et al. [209] stated that the reason for the generation of unstable large melt pools that causes balling is high laser power and low scanning speed. This also increases the production cost and time. The balling defect can be dealt with by employing low laser energy and high scanning rates. In the case of LPBF for Al, it is not possible to eliminate the effect of oxides. Hence, further research work is required to come up with solutions to control oxide formation. Table 12 gives detail for the commercially available $\mathrm{Al}$ alloys, which are used in production with respect to applications, key properties, and heat treatment along with constituent elements in different series [154].

\subsection*{3.3.1. LPBF processing window for $\mathrm{Al}$ alloys}
The map of processing windows for $\mathrm{Al}$ and its alloys, particularly for pure $\mathrm{Al}, \mathrm{Al}-\mathrm{Mg}$ and $\mathrm{AlSi}_{12}$, was drawn out by conducting experiments with laser power ranging from $20 \mathrm{~W}$ to $240 \mathrm{~W}$, scanning speed $20 \mathrm{~mm} / \mathrm{s}$ to $250 \mathrm{~mm} / \mathrm{s}$ and at a constant hatch spacing of $0.1 \mathrm{~mm}[210,211]$. Four regions were identified as no marking, partial marking, good consolidation and excessive balling (Fig. 11).

Fig. 12 shows a similar trend for all the powders that were investigated. There were slight differences in the boundaries parting the different regions. The regions of partial marking were mostly including parts that were produced with very low strength. The region of good consolidation had samples fabricated with good coherent bonding and having high strength. Hence, good consolidation should be considered the appropriate region for manufacturing multi-layered parts via LPBF. The region of excessive balling comes with the unwanted defect of balling, causing large melt pools-the reasons for forming such regions are discussed below.

\begin{enumerate}
  \item The region of no marking is influenced by the short interaction time period between the laser and the material and low energy density, which causes poor inter particulate bonding. Poor bonding can also result from using high scanning speeds for low laser powers during the LPBF process.
\end{enumerate}

Table 10 - Properties of LPBF produced Ti alloys composites with those produced by different methods.

\begin{center}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
Material Type & Condition & Vickers Hardness/VH & $\sigma_{0.2} / \mathrm{MPa}$ & $\sigma_{\mathrm{UCS}} / \mathrm{MPa}$ & $\varepsilon_{\max } / \%$ \\
\hline
Ti-8.35 vol- \% TiB & LPBF & 402 & 1103 & 1421 & 17.8 \\
\hline
CP-Ti & Casting/ECAP & 210 & 700 & 900 & 35 \\
\hline
$\mathrm{Ti}-6 \mathrm{Al}-4 \mathrm{~V}$ & Superplastic forming/annealed & 346 & 1000 & 1300 & 10 \\
\hline
\end{tabular}
\end{center}

Note: $\sigma_{0.2}$ is the yield strength, $\varepsilon_{\max }$ is the maximum strain, $\sigma_{\mathrm{UCS}}$ is the ultimate compressive strength.

\begin{enumerate}
  \setcounter{enumi}{1}
  \item The region of partial marking is characterized by parts fabricated with a large amount of porous defects. This is also due to the adoption of low energy density, which in turn is due to low laser powers and high scan speeds. This combination of processing parameters leads to the formation of an inadequate liquid phase that ultimately results in poor inter particulate bonding.

  \item The region of good consolidation is characterized by the fabrication of parts through LPBF with fair level of density, almost $60 \%-80 \%$. The energy density range employed in this region leads to an increase in the powder bed temperature and a decrease in the melt pool's viscosity, hence improving the densification of the fabricated part. This result is obtained due to the adoption of higher energy density which leads to the generation of an adequate liquid phase that promotes ed complete melting of powders [212].

  \item The region of excessive balling is characterized by parts fabricated via LPBF with fully dense parts but a very rough surface. This result is obtained due to the adoption of high laser energy density, which is adopting high laser power at low scanning rates. These findings were also confirmed by Zhang et al. [191], who also show the excessive generation of liquid phase leading to balling.

\end{enumerate}

\subsection*{3.3.2. Hard metals}
Hard metals manufacturing has always been difficult due to the high requirement of time, energy, and materials. The advent of SLM has led to more convenience in manufacturing these materials. Understanding the relationships between the densification of the fabricated part and the process parameters plays an important role in concluding the microstructures and the fabricated parts' mechanical properties. Among all the metals, $\mathrm{Ti}, \mathrm{Mg}$ and $\mathrm{Al}$ are widely considered for application in various industries such as aerospace and biomedicals because of their properties. This difference in microstructure in the case of Ti can be due to the different laser parameters, mainly laser scanning speed. In the case of $\mathrm{Mg}$, microstructural evolution is monitored by the specific laser energy input and cooling rates. Mechanical properties like tensile strength, compressive strength, hardness and microhardness

\section*{Table 11 - Properties of Ti-6Al-7Nb part manufactured}
 by LPBF and casting.\begin{center}
\begin{tabular}{lcccc}
Processing method & E/GPa & $\sigma_{0.2} / \mathrm{MPa}$ & $\sigma_{U T S} / \mathrm{MPa}$ & $\varepsilon_{f} / \%$ \\
\hline
LPBF & 109 & 1110 & 1267 & 7.28 \\
Casting & 110 & 847 & 976 & 5.1 \\
\hline
\end{tabular}
\end{center}

Note: $\sigma_{0.2}$ is the yield strength, $\varepsilon_{\mathrm{f}}$ is the fracture tensile strain, $\sigma_{U T S}$ is the ultimate tensile strength and $\mathrm{E}$ is the young's modulus. properties of LPBF fabricated samples show that LPBF can produce samples with superior properties to those manufactured using traditional processes such as casting. It has also been found that the addition of other metals to develop an alloy proves to be more effective when processed under the LPBF process.

\section*{4. LPBF process parameters}
In any manufacturing process, the influence of process parameters plays a significant role in the manufacturing process setup conditions. Table 13 depict the ranges of process parameters such as laser power, scan speed, spacing, and particle size, along with significant effects.

\subsection*{4.1. Effects of LPBF process parameters on densification}
LPBF is considered a very complex process due to the involvement of many process parameters. It is necessary to achieve the correct combination of such parameters while processing to get a fully dense part. Some of the critical parameters associated with LPBF can be categorized as given in Fig. 13.

4.1.1. Effects of laser processing parameters on densification The individual parameters of laser power $(P)$, scan speed $(u)$, hatch spacing (h) and layer thickness (d) can be combined to form a single equation:

$\in=\frac{P}{\text { u.h.d }}$

where, $\in$ is nothing but the laser energy density [213]. The variation in the parameter of laser energy density, which depends on the combination of varying parameters, influences the densification of the part fabricated through LPBF. Since every material has different characteristics in terms of thermal and mechanical behavior, separate experimental work has been carried out for each material to draw the map of the processing window $[214,215]$. One of such experiments was conducted by Zhang et al. [191] on $\mathrm{Mg}-9 \% \mathrm{Al}$ alloy. They studied the effect of laser energy density on the density of the part produced via a continuous Nd: YAG laser. They recorded an increase in the laser energy density from 7.5 to $15 \mathrm{~J} / \mathrm{mm}^{2}$, significantly improving the relative density from 74.5 to $82 \%$. In low laser energy density cases, mainly due to high scan speeds, partial melting occurs, leading to porosity and generation of discontinuous tracks. When the laser energy density was increased, better melting of powders was observed, leading to the disappearance of pores and a smooth surface finish. But, if the laser energy density is increased further to\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-19}

Fig. 9 - SEM images for the microstructures of the SLM-produced Ti-TiB composite at different magnifications: $(a, b)$ crosssectional views and (c, d) longitudinal views showing needle-shape TiB particles within the Ti matrix. White arrows indicate TiB particles [158].

approximately $20 \mathrm{~J} / \mathrm{mm}^{2}$, density is reduced. This is because though sufficient energy is there to generate the liquid phase, low scan speed causes spheroidization and the molten pool breakdown [194].

Small circumference to length ratio and unstable small dimensional molten pools are developed when the laser energy is not adequate to generate the liquid phase [216,217]. This unstable molten pool finally hits the necking stage and breaks into balls, causing balling. A typical phenomenon was observed in case of LPBF fabricates CP-Ti [106] and Ti-24 N-4Zr-8Sn alloy [140] which highlighted that if the powder is fully melted, there was no further benefits in increasing the laser energy density because any more increase in the laser energy density resulted in balling, poor surface finish and lower density. In the LPBF process, much of the research work highlighted that low energy density, due to low laser power and high scanning speed, could not generate sufficient liquid phase for the powder to bond together, resulting in poor densification due to partial melting. But as the laser power is increased at lower scan rates, there is a significant rise in laser energy density which now provides adequate energy for the generation of liquid phase for powders to bond together. This results in higher densification as the process approaches full melting. Hence, it can be concluded that high temperature can be achieved with appropriate increments of laser energy density. This increases the densification of the produced part. It reduces surface tensions and viscosity, which favors the generation of connective streams and flows within the molten pool.\\
Wei et al. [218] worked to understand the effects of processing parameters on LPBF processed AZ91D alloy. They observed that with the increase in both the hatch spacing and scan speed, there is a significant drop in the fabricated part's density. Lowering the scan speed at constant laser energy provided more interaction between the laser and the material and better densification [219]. A new term, overlapping scan lines, comes in the picture when hatch spacing, also known as scan spacing, is discussed. It usually depicts the amount of overlap of the consecutive scan lines. The hatch spacing's usual selection is such that it is in the range of half and the entire width of the molten pool for strong bonding of adjacent scan tracks [90]. If a scan spacing of size wider than the laser spot is chosen, there is a reduction in the overlapping, and poor bonding of the adjacent scan tracks was observed, leading to porosity. In an experiment with AZ91D parts, a maximum density of $99.52 \%$ was recorded for the hatch spacing of $90 \mu \mathrm{m}$ at a low scan speed of $0.33 \mathrm{~m} / \mathrm{s}$. The combination of parameters giving the laser density of approximately $166.7 \mathrm{~J} / \mathrm{mm}^{3}$ was enough to penetrate any oxide layers and give maximum dense (more than $99.5 \%$ ) parts. In another experiment [220] to understand the effect of scan speed with the ZK60 alloy, the investigation was done by keeping the other parameters constant. Laser power, scan spacing, layer thickness, and laser spot size were kept constant as $200 \mathrm{~W}$; $80 \mu \mathrm{m}, 20 \mu \mathrm{m}$, and $150 \mu \mathrm{m}$, respectively. Within the range of scan speed from 100 to $900 \mathrm{~mm} / \mathrm{s}$, it was seen that the maximum dense of $94.05 \%$ was achieved at a speed of $300 \mathrm{~mm} / \mathrm{s}$. Severe vaporization and burning of materials were seen at a low scan speed of $100 \mathrm{~mm} / \mathrm{s}$. At scan speed higher

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-20}
\end{center}

Fig. 10 - Processing window map of $\mathrm{Mg}-9 \% \mathrm{Al}$ in accordance with scan speed and laser power.

than $500 \mathrm{~mm} / \mathrm{s}$, powders didn't undergo complete melting and caused porosity and reduced density up to $82.25 \%$. It was also observed that melt tracks have an evident transition from continuous to discontinuous track on continuously increasing the scan speed [219,221]. Fig. 14 shows the effect of scanning speed on balling [222].

Another critical parameter in the LPBF process is layer thickness. It affects the parts' mechanical properties and dimensional accuracy [223]. Establishing an appropriate layer thickness concerning other processing parameters is crucial. If the layer thickness is large, the powders' complete melting will not be possible because there will not be enough laser power to penetrate the powder bed. This results in large voids and pores, which decreases the density of the produced part. Hence, an optimum layer thickness value should be for fine resolution and better bonding between the intermediate layers with fewer defects such as porosity. Small layer thickness helps in the better fusion of interlayers by allowing more laser energy to penetrate the powder bed. This exposes the previously melted layers for multiple remelting, increasing the density and the wetting properties [220].

Olakanmi et al. [224] observed in their experiment with $\mathrm{AlSi}_{12}$ that there is a particular layer thickness value. Such a product with no porosity proved to have excellent microstructural properties because the multiple remelting of layers proved to showcase excellent inter-layer bonding with minimum balling. Fig. 15 shows the TEM images revealing the effect of molten metal solidifying to give the fine resolution of grains [225]. Most of the gas bubbles escape to the surface and collapse when the molten metal solidifies to provide a fine resolution of grains.

Another experiment was done by Salavni et al. [190] on Mg powder to understand the effects of layer thickness. With all the process parameters set constant, scan speed ranged from 10 to $200 \mathrm{~mm} / \mathrm{s}$, and layer thickness varied from 150 to $300 \mu \mathrm{m}$ They showed a critical value of layer thickness beyond which successful remelting layers is impossible. If done so, the disrupted and irregularly finished surface is generated. When lower layer thickness was employed, surfaces with no defects and no heat-affected zones were obtained. The primary reason is that since there is less material to be melted with a small layer thickness, enough energy is to melt the powders completely. Also, the heat conducted melts the neighboring particles. So, when remelting occurs, the melt pool's enough wetting doesn't let spheroidization occur [194,226]. Agarwal et al. [227] stated that the minimum layer thickness, which will give a porous free part, is nothing but the maximum size of the particle deposited in the powder bed and also depends upon the powder delivery mechanism. Olakanmi et al. [228] stated that if a layer thickness less than the minimum value is set, there will be difficulties in the homogenous distribution of the powder, which would impact the surface finish.

All combined, it can be stated that the densification is increased with rise in laser power and reduction in scan speed, layer thickness and scan spacing. This result is also verified for other materials and alloys such as Al12Si [229] and $\mathrm{Ni}-\mathrm{Cu}$ alloy. SEM photographs of the microstructure of AlSi12 alloy generated by the L-PBF process under varying laser power and scanning velocity are shown in Fig. 16. The - $\mathrm{Al}$ and Si phases are the brilliant and dark phases, respectively. All of the samples had a cellular microstructure with a primary-Al phase surrounded by a eutectic - $\mathrm{Al} / \mathrm{Si}$ microstructure. Microstructural morphology is widely found in Al-Si alloys produced via L-PBF. Although the primary -Al phase was slightly coarsened when a high-power laser was used at a low scanning velocity, the morphology of cellular microstructure did not change substantially depending on the laser settings. At the FE-SEM resolution, no phases were found inside the elongated -Al phase [230].

\subsection*{4.1.2. Effects of scanning strategy}
The generation of non-uniform thermal gradients leads to LPBF defects such as porosity, residual stresses, and poor surface finish. A scanning strategy was used to tackle the thermal gradient problem during the heating and cooling phases $[231,232,233,234]$. Several researchers devised various

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-21}
\end{center}

scanning strategies to produce fully dense parts with almost no defects. Some of the common strategies that are widely accepted are unidirectional, bidirectional and island/chessboard strategies as shown in Fig. 17. Some strategies were developed by modifying a few of these basic ones. A rotation of certain angles such as $60^{\circ}$ [235] or $90^{\circ}$ [236] or a shift for a particular distance between the layers [237] could be some modifications. The unidirectional strategy is the simplest but gives the worst density results among other methods [238].

Fig. 18 shows different kinds of scanning strategies applied in AlSi10Mg. Scanning strategies A (unidirectional), B (bidirectional), C (bidirectional rescanning with $90^{\circ}$ rotation in scanning direction) and $\mathrm{D}$ (island scanning) achieved relative density of $99 \%, 98.9 \%, 99.4 \%$ and $98.2 \%$ respectively [235]. Dewidar et al. [231] categorized the scanning strategies as standard, diagonal, and perimeter scanning for single-layer melting of powders (Fig. 19). Su et al. [232] tried employing different scan strategies to understand their impacts (Fig. 20). It was observed that the perimeter scan strategy did not give relevant results, while the diagonal and standard scan strategies gave pretty much the same result [231].

One another alternative to reduce defects such as porosity is remelting. It is nothing but melting the same powder bed twice or thrice before a new powder layer is introduced. This step eliminates surface defects and surface contaminants, along with any oxides. This also helps obtain higher densification when other parameters cannot attain the full density. The only drawback is that its time is taking and energyconsuming. To avoid any residual stresses or cracks, it is proposed to carry out the remelting process under low energy density [238]. It was also concluded that remelting on densification depends on the careful selection of other parameters [239].

Another four kinds of strategies for remelting on TiC/316 L, investigated by AlMangour et al. (Fig. 20) [235], gave relative densities of $92.48 \%, 96.04 \%, 86.91 \%$, and $96.40 \%$, respectively. It was concluded that higher densification was achieved on remelting by adopting strategies I and II, but strategy III gives lower densification than strategy I. It was also supposed that remelting with the same direction gave aggravated texture, while remelting with rotation in the scanning direction had better texture results. Hence rotational remelting is highly suggested.

\subsection*{4.2. Effect of powder parameters on densification}
The properties and quality of the powder used in the LPBF process play a huge role in monitoring the stability of the process and determining the properties of the parts produced. When we consider the characteristics of the powder, it is generally talked about the size, shape, composition, internal porosity, and surface morphology. Physical variables such as apparent density (degree of how good the powder can be packed) and flowability (the degree of the tendency of powders to flow) are also considered [240]. The powder morphology is one character that monitors all the other process parameters such as packing of powders, flowability, and the nature of the thermal effects. They also play an essential role in choosing the layer thickness and other parameters for the process [241].\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-22(1)}\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-22}

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-22(2)}
\end{center}

Fig. 11 - A map for processing window for parts fabricated in (a) air atomized Pure $\mathrm{Al}$ (b) gas atomized pure $\mathrm{Al}$ (c) water atomized Al-5.6 Mg (d) water atomized Al-6Mg (E) Gas atomized Al-12Si. [210] (1: No marking; 2: Partial Marking; 3: Good Consolidation 4: Excessive Balling).

\subsection*{4.2.1. Effect of flowability, absorptivity, reflectivity and}
 conductivityIt is well known that Al powder has poor flowability because of its low density and non-spherical-shaped powders. This poor flowability makes the task of depositing $\mathrm{Al}$ powders very tough, which is an essential LPBF process. Also, surface oxides are developed during atomization, restricting the surface tension forces that try to spheroidize the particles. This contributes to poor flowability [242]. So, spherical powders should be employed for better densification and accuracy because it improve flowability [240]. Another critical factor is that the powder should be free from significant defects such as pores as they can cause low fusion among the particles, directly influencing the density. The powders' size also affects fine and narrow-size particles that tend to come together and fuse, opposite with larger and coarse particles [243].\\
One of the LPBF process's essential procedures is the laser beam's interaction with the powder particles and the absorption of laser energy by the powder. An absorptance, calculated as the ratio of absorbed energy radiation to incident energy radiation, plays a key role in giving accurate results in the LPBF process. Determining the absorption of powders can be of prime importance because then we can avoid the supply of extra laser energy, which causes superheating and evaporation of the powders. It can help to map out a more effective processing window [244]. The layer of powders first absorbs the laser energy on the surface leading to the rise of temperature on the surface. The heat then starts to flow to the center of the particles, leading to the development of thermal cycles. The transfer of heat takes place until a steady state is reached, and it also depends on the properties of the surrounding powder particles [240]. If the absorptivity of powders in a

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-23}
\end{center}

Fig. 12 - Map of the processing window for $\mathrm{Al}$ powder showing different microstructures with varying process parameters. [210] (1: No marking; 2: Partial Marking; 3: Good Consolidation 4: Excessive Balling).

particular local area differs, it will vaporize the powder. $\mathrm{Al}$ is highly reflective when it comes to light in the infrared range. For a $1 \mathrm{~mm}$ wavelength laser, $\mathrm{Al}$ almost reflects everything and absorbs only around 7\% of the energy [245]. Though the absorption of the whole powder bed will be higher than the expected value due to multiple absorption and reflection, it is essential to supply laser energy more elevated than that calculated to overcome the reflectivity problem. Also, absorptivity will be different between the already scanned layers and the fresh neighboring particles. Hence temperature gradients will be developed in the overlapped tracks scenario, which might cause balling.

$\mathrm{Al}$ is also known for its high conductivity. In such cases, the heat energy supplies quickly conduct away to the nearby already scanned and solidified part. This can have multiple impacts. Firstly, more energy consumption will be there than for low conductivity materials. The melt tracks' width for higher conductive material will be much more than low conductive materials because heat is conducted away from the neighboring areas. $\mathrm{Mg}$ has the property of both high conductivity and reflectivity. But the deposited powder has relatively poor conductivity compared to the solid substrate [240]. When the heat is applied, it flows slowly, leading to the overheating of the melt pool. This influences the melt pool's size and creates a difference in densities between the powder and the solid [245]. Another drawback of high conductivity and reflectivity is that it is challenging to control and monitor the LPBF process.

\subsection*{4.2.2. Powder feedstock}
Persistent defect creation in produced components, high material costs, and a lack of consistency in powder feedstock are all major roadblocks to mainstream adoption of metal additive manufacturing (AM). Understanding how feedstock qualities change with reuse and how these impacts build mechanical performance is critical for producing more dependable, complex-shaped metal parts. If powder particles engage with the energy source but are not consolidated into an AM component, they can undergo a variety of dynamic thermal interactions, resulting in varied particle behavior. Heiden et al. [246] reported a comprehensive analysis of $316 \mathrm{~L}$ powder characteristics from the virgin phase via 30 powder recycles. According to the results, the pristine condition has more diversity in component stiffness. With recycling, the feedstock's size distribution, bulk composition, and hardness changed slightly, but particle shape, microstructure, magnetic properties, surface composition, and oxide thickness changed significantly. Dipl.-Ing et al. [247] compared the surface quality, part density, and mechanical properties of AlSi10Mg parts produced by LPBF, using different particle size distributions and morphologies. The results revealed that powder particle morphology and manufacturing process significantly affect the part bulk density, surface quality, and layer densities of powders. Powder feedstock quality is critical in the LPBF process because the technique relies on small layers of powder being distributed and selectively melted to create 3D metallic components. Particle morphology, particle size distribution, apparent density, and flowability are the only parameters used to evaluate powder quality in additive manufacturing. Recent research suggests that these strategies may not be the most suited. The difficulty of investigating aluminum particles worsens since their complicated cohesive behaviors affect their flowability. Muñiz-Lerma et al. [248] studied the powder spread density, moisture sorption, surface energy, work of cohesion, and powder rheology along with conventional powder characterization assessments. The involvement of small particulates enhances wetness pick-up, enhancing total particle surface energy and inter-particle cohesion, according to this research. This impact obstructs powder flow and, as a result, the uniform layer spreading required for optimal printing. Moisture adsorption, surface energy, and cohesion properties are reduced when spherical particles are more significant. This finding suggests that problematic powder feedstocks can be adjusted for LPBF by adjusting particle distribution, size, and shape. Santecchia et al. [249] reported that recycling the unfused powder from a building job is critical to making LPBF more cost-effective and environmentally benign. However, more profound knowledge of the complete process is required because the laser-powder interaction involves complex physical phenomena and generates by-products that could compromise the feedstock and end construction part's integrity. Hupfeld et al. [250] reported that adding nanoparticles in the feedstock is the ultimate solution for tailoring the feedstock material properties for printable and obtaining the desired properties of the printed parts.

Additives have been discovered to be an effective way to improve feedstock materials' mechanical and functional qualities. Further, Kusoglu et al. [251] also confirmed that the alloying of particle additives in feed stock significantly solved the processing and metallurgic issues, such as anisotropic microstructure, segregations, and fracture formation in asbuilt parts during L-PBF. It was also reported that Al-alloy is the third most studied material in LPBF process. The alloying of particle additives in feed stock was found promising and industrializing to improve the mechanical properties and avoid crack sensitivity in the as-built. Furthermore, Kusoglu

Table 13 - Process parameters of (LPBF) selective laser melting.

\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
S.no. & Parameter & Range & Effect \\
\hline
\multirow[t]{2}{*}{Laser Related Parameters} & Laser Power & $10 \mathrm{~W}-110 \mathrm{~W}$ generally & \begin{tabular}{l}
Mainly responsible for melting. Laser \\
power increase with the increase in the \\
melting point of powder used. \\
\end{tabular} \\
\hline
 & \begin{tabular}{l}
Pulse Duration/ \\
PulseFrequency \\
\end{tabular} & \begin{tabular}{l}
$20-30 \mu \mathrm{m}$, till $300 \mu \mathrm{m}$ \\
in typical cases \\
\end{tabular} & \begin{tabular}{l}
Laser power, spot size, scan speed, and \\
bed temperature determine the energy \\
input needed to fuse the powder into a \\
useable part. The longer the laser dwells in \\
a particular location, the deeper the fusion \\
depth and the larger the melt pool \\
diameter. \\
\end{tabular} \\
\hline
Scan Related Parameters & \begin{tabular}{l}
Scan Speed \\
Scan Spacing \\
Scan Pattern \\
\end{tabular} & $100-600 \mathrm{~mm} / \mathrm{s}$ generally & \begin{tabular}{l}
Scan pattern and scan strategy can have a \\
profound impact on residual stress \\
accumulation within a part. For instance, \\
if a part is moved from one location to \\
another within a machine, the exact laser \\
paths to build the part may change. These \\
laser path changes may cause the part to \\
distort more in one location than another. \\
Thus, a part can build successfully in one \\
location but not in another location in the \\
same machine due simply to how the scan \\
strategy is applied in different locations. \\
\end{tabular} \\
\hline
\multirow{7}{*}{}\begin{tabular}{l}
Powder Related \\
Parameters \\
\end{tabular} & Particle Size & 15-50 microns & Powder shape, size, and size distribution \\
\hline
 & Particle Shape & Spherical & strongly influence laser absorption \\
\hline
 & \begin{tabular}{l}
Particle size \\
Distribution width \\
\end{tabular} & 1.38 microns & \begin{tabular}{l}
characteristics as well as Powder bed \\
density powder bed thermal conductivity \\
\end{tabular} \\
\hline
 & Layer thickness & $20 \mu \mathrm{m}-100 \mu \mathrm{m}$ & \begin{tabular}{l}
and powder spreading. Finer particles \\
provide greater surface area and absorb \\
laser energy more efficiently than coarser \\
particles. \\
\end{tabular} \\
\hline
 &  &  & Uniformity in microstructure \\
\hline
 & Powder Bed Density & - & \begin{tabular}{l}
As governed by powder shape, size, \\
distribution, and spreading mechanism, \\
the powder bed density can strongly \\
influence the part quality. Powder bed \\
densities typically range between $50 \%$ and \\
$60 \%$ for most commercially available \\
powders but may be as low as $30 \%$ for \\
irregular ceramic powders. Generally the \\
higher the powder packing density, the \\
higher the bed thermal conductivity and \\
the better the part mechanical properties \\
\end{tabular} \\
\hline
 & Material Properties &  & \begin{tabular}{l}
Mechanical properties of material after \\
printing generally depends upon \\
combination of parameters \\
\end{tabular} \\
\hline
Temperature Related & Powder Bed Temperature &  & Powder bed temperature and varies \\
\hline
Parameters & Powder Feeder Temperature &  & \begin{tabular}{l}
depending upon the absorptivity \\
characteristics of the powder bed, which \\
is influenced by material type and powder \\
shape, size, and packing density. Infrared \\
or resistive heaters are generally used. \\
\end{tabular} \\
\hline
\end{tabular}
\end{center}

et al. [252] extended the study and collected the literature on powder feedstocks for LPBF processing of polymers. Carbonbased compounds are commonly used in applications that require mechanical characteristics. In addition, for L-PBF of polymer powder feedstocks, carbon fibers, Ca-phosphates, and $\mathrm{SiO} 2$ are the most frequently reported additions.

\subsection*{4.2.3. Effect of particle shape, size and distribution on}
 densificationSince complete melting is the critical objective of the LPBF process, the effect of particle size and shape are generally considered to be of less value. But much work has been done on understanding the effects of powder properties such as shape, size, surface morphology, chemical composition, and size distribution on the SLS process $[253,254]$. However, better densification always enhances the LPBF result, and we would generally try to understand the effect of powder size in additive manufacturing processes, especially SLS.

Olakanmi et al. [224], in their work with laser sintering of $\mathrm{Al}-\mathrm{Si}$, stated that powder particle size and size distribution have a crucial influence on the densification of the final part. It was pointed out that smaller particles were sintered quickly,

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-25(1)}
\end{center}

Fig. 13 - Categorization of process parameters.

and densification shall happen by neck formation at points of contact among the particles. To obtain better sintering for the powders, blending the powders of different sizes is necessary. However, fine powder particles help in increasing the packing density and the sintering response. Small particles experience stress due to sintering in a much larger magnitude than that of the larger particles. Therefore, the small particles are very prone to defects. This also means that the larger particles restrict the shrinkage of the small particles, which gives rise to cracking around the large particles [255]. The response of

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-25}
\end{center}

Fig. 14 - The effect of the scanning speed on the diameter of bead caused by balling phenomenon (Raw Powder $\mathrm{D} 50=6.43 \mu \mathrm{m}$ ) [222]. different-sized particles during processing is different. Largersized particles need more time and energy to melt. Hence, the parameters must be optimized to avoid local overheating or partial melting, resulting in porosity [256]. Though the smallsized particles can give better results than coarser and large particles for even low laser energy density value, they can get blown away by the gas in the shielding chamber and adhered to the surface. This disturbs the distribution of the next powder bed [194].

It was stated by Liu et al. [257] that the shape of the powder particle also influences the densification process. They found that the thermal expansion between the $\mathrm{Al}$ powder and the oxide layer caused the oxide to fracture. The characteristics of this fracture differed for spherical and irregularly shaped particles. Niu et al. [258] experimented on water atomized angular particles and gas atomized spherical particles. They found that the SLS of gas atomized spherical particles gave a homogenous and dense single layer. Irregularly shaped particles and high oxygen content caused the porous outcome for the water atomized particles. Olakanmi et al. [210] studied the effect of powder properties on densification kinetics of $\mathrm{Al}$ powders undergoing the LPBF process. It was found that the densification kinetics is primarily affected by the oxide constitution and the shape and density of the powders used. Olakanmi et al. [224] that the apparent and tapping density of the part results from the thermal cycles that happened during the process and can easily be controlled by using particle of different sizes. This technique is often known as bimodal or trimodal powder distribution.\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-26}

Fig. 15 - TEM bright-field images of the SLMed AlSiMg1.4 alloy showing (a) the dislocation substructure in an $\alpha-\mathrm{Al}$ grain and (b) a high density of dislocation tangles in the interior of a subgrain [225].

\subsection*{4.2.4. Effect of alloying elements on densification}
An experiment was conducted by Zhang et al. [259] to understand the effect of adding $\mathrm{Ni}$ as an alloying element to tungsten powder. LPBF process was carried on titanium powders with $10 \%, 20 \%$, and $40 \%$ Ni by weight. The conclusion was that the addition of Nickel improved the density of the titanium-nickel alloy. A similar experiment was carried on by Dadbaksh et al. [260] to understand the addition of $15 \% \mathrm{Fe}_{2} \mathrm{O}_{3}$ by weight to different $\mathrm{Al}$ alloys such as pure $\mathrm{Al}, \mathrm{AlMgSiCu}$, and $\mathrm{AlSi10Mg}$. The conclusion was that adding $\mathrm{Fe}_{2} \mathrm{O}_{3}$ worked for the consolidation performance of the $\mathrm{Al}$ alloys (Fig. 21). The addition also helped in lowering defects like porosity and oxide layer breakdowns.

Wang et al. [261] studied the effect of alloying elements X ( $\mathrm{Cr}, \mathrm{Mn}, \mathrm{Mo}, \mathrm{Ni}, \mathrm{Si}$ ) on the interface stability of $\mathrm{TiC}(001) / \gamma-\mathrm{Fe}$ (001) in TiC/316 L stainless steel composite formed by selective laser melting. The adhesion work, bond length, interlayer distance, and electrical properties of $\mathrm{Cr}_{4}, \mathrm{Mn}_{4}, \mathrm{Mo}_{4}, \mathrm{Ni}_{1}$, and $\mathrm{Si}_{1}$ interface models were shown to be more conducive to improving interface bonding strength, and $\mathrm{Cr}^{-}$, $\mathrm{Mn}-$, and Modoped interfaces are more stable than other interfaces. The doping of an alloy atom is represented by both the $\mathrm{X}_{1}$ and $\mathrm{X}_{4}$ structures but only in distinct crystallographic positions. As a result, adding $\mathrm{Cr}, \mathrm{Mn}, \mathrm{Mo}, \mathrm{Ni}$, and $\mathrm{Si}$ to $\mathrm{TiC}$ will accelerate $\mathrm{Fe}$ heterogeneous nucleation, increase TiC's heterogeneous nucleation potential, and improve the TiC/316 L stainless steel composite's interfacial bonding strength. Wei et al. studied

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-26(1)}
\end{center}

Fig. 16 - SEM images showing microstructure in AlSi12 alloys manufactured by L-PBF process under various laser power and scanning velocity [230].

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-27}
\end{center}

Fig. 17 - Different kinds of scanning strategies.

the effect of $\mathrm{Zn}$-content on the densification behavior, microstructure, and mechanical property of $\mathrm{Mg}-\mathrm{Zn}$ binary alloys [262]. The quantity of $\mathrm{Mg}_{7} \mathrm{Zn}_{3}$ rose in lockstep with the rise in $\mathrm{Zn}$ concentration, while its morphology gradually changed from granular to virtually reticular. According to hardness and tensile testing, only the $\mathrm{Mg}-1 \mathrm{Zn}$ sample has equivalent mechanical qualities to the as-cast counterpart. Mechanical characteristics of LPBF-processed alloys were dramatically reduced at increasing $\mathrm{Zn}$ concentrations, owing to the worsening of the densification degree. Kimura et al. [263] studied the effect of silicon ( $\mathrm{Si}$ )-content ( $\mathrm{Si}=0,1,4,7,10$, 12, and 20 mass\%) on densification, mechanical and thermal properties of Al-xSi binary alloys fabricated using selective laser melting. The ultimate tensile strength and proof stress increased as the silicon content increased, whereas elongation and thermal conductivity declined. With increasing silicon content, higher amounts of the crystallized phases of silicon were attributed to these mechanical qualities.

\subsection*{4.3. Alternate parameters}
\subsection*{4.3.1. Atmospheric conditions}
The presence of oxygen in the atmosphere during the LPBF process, especially metals, can prove disastrous as it forms oxide and will also lead to balling formation [264,265]. Hence, it is very important to control the LPBF process atmosphere so undesirable reactions can be avoided. The controlled atmosphere can also lead to the initiation of some desired responses. For example, adding nitrogen to $\mathrm{Al}$ to form AlN prevents oxidation and improves dimensional stability [266]. Wu et al. [267] studied the effect of oxygen content on the microstructure of $\mathrm{Ti}$ alloy, $\mathrm{Ti}-25 \mathrm{~V}-15 \mathrm{Cr}-2 \mathrm{Al}-0.2 \mathrm{C}$. The shielding gas was used as argon, and the oxygen concentration in the argon shielded chamber was less than $5 \mathrm{ppm}$. The results showed that the microstructure of parts processed in the air has large dendrite-like structures and was coarser. At the same time, the part produced in the shielded atmosphere had uniformly distributed finer grains. Schaffer et al. carried out the study of atmospheric conditions on the sintering of AL and its alloys [266]. And they pointed out that vacuum, nitrogen, argon and hydrogen gases were commonly used as shielding gases. It was found that for the alloy AL-Mg-0.5Si$0.2 \mathrm{Cu}$, nitrogen gave better results, while $\mathrm{Al}-4.5 \mathrm{Cu}-0.5 \mathrm{Mg}$ $0.2 \mathrm{Si}$ had better processing results with a vacuum shielding chamber. The basic reason for the differences was pointed out to be the different cooling rates after the process.

\subsection*{4.3.2. Densification behavior and process variables}
The discussion of the relationship between the densification behavior and processing parameters can be focused on laser energy density, which is nothing but a combination of variations of parameters. Generally, it was observed that with an increase in the value of laser energy density, smooth surfaces were observed with minimum pores. This is because of the better melting and flow of the liquid phase into the voids. The decrease in the scanning speed gave more time for the laser beam interaction and the powder, resulting in full melting and, consequently, gave better density. There is also an\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-27(1)}

Fig. 18 - Standard, diagonal and perimeter scan strategies.

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-28(4)}
\end{center}

Fig. 19 - Different scan strategies: The numbers depict the scanning sequence and arrows depict the scanning direction.

increase in the density with decrease in the hatch spacing as the flowing and distribution of liquid molten metal was increased when the scan lines were brought closer to each other. Higher layer thickness will result in a lack of laser energy density for the complete melting of the powder bed, and shallow layer thickness will increase production and cost time. Hence, an optimum layer thickness needs to be calculated to balance both extremes. The powder properties also influence the densification behavior. The quality of the powder is determined by size, shape, surface morphology, composition, and amount of internal porosity and via physical variables, such as flowability, absorptivity, reflectivity, and thermal conductivity. Also, keeping the atmosphere during LPBF processing under control is very important because it prevents undesirable reactions and reduces oxide on the metal surfaces, resulting in defects such as balling.

\section*{5. Properties of LPBFed parts}
\subsection*{5.1. Microstructures}
The microstructures in the LPBFed parts are governed by short cooling cycles. The rapid solidification and the directional solidification give out finer microstructures for the LPBFed part. The phase compositions, phase percentages, and grain sizes can also be monitored by controlling the process parameters. The performance-based on the quality of the part

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-28(3)}
\end{center}

I- Alternate hatches, single pass of the laser beam

\begin{center}
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\end{center}

III- One direction hatches, double pass of the laser beam

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-28(2)}
\end{center}

II- Alternate hatches, double pass of the laser beam\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-28}

Fig. 20 - Four different strategies for remelting used in [235].

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-29}
\end{center}

Fig. 21 - Effect of addition of $15 \% \mathrm{Fe}_{2} \mathrm{O}_{3}$ by weight on densification for different $\mathrm{Al}$ alloys [260].

produced strongly depends on the thermal cycles it experienced during its fabrication. The thermal processes consist of high cooling and heating rates, large temperature gradients, sudden temperature rise, and many more. The final microstructures of the LPBFed part mainly depend on the high solidification rate [268].

All the process parameters, including layer thickness, scan speed, hatch spacing, and laser power, strongly influence the microstructure development [269]. Many studies have been done to understand the effect of process parameters on the cooling rate of the process and subsequently on the microstructures developed. Do et al. [270] and Han et al. [271] studied the effects of laser energy on the microstructure development of LPBF processed Ti64. And both the research work showed the same result of the presence of martensitic structures with all laser energy values. There was observed a decrease in cooling rate and an increase in the size of martensite grains with the laser energy density. Particularly, it was also observed that the increase in laser energy density decreases the width of the $\alpha^{\prime}$ and the spacing between them but increases columnar grains' width. Many pieces of research with LPBF on Ti-6Al-V4 showed acicular $\alpha$ ' martensite grains in columnar prior- $\beta[130,131,132,133,134]$. The main reason for the generation of this microstructure is selecting process parameters that would give the cooling rate greater than $410 \mathrm{~K} / \mathrm{s}$ $[135,136]$. It was also observed for the microstructures of $\mathrm{Mg}$ processed parts that a lower energy density, due to high speed and low power, resulted in more refined grains due to high cooling rates. And by reducing the speed and increasing the power, low cooling rates and coarser grains can be produced.

Apart from varying the process parameters, the nature of the developed microstructure also depends on the time of interaction between the laser and the powder. This relationship between the interaction period and the laser energy density gives rise to the temperature gradients (G), the solidification rate (R), and the cooling rate. It was observed that increasing the $G / R$ ratio inclines dendritic formation to cellular dendritic grains [272]. Also, a higher cooling rate supports the concept of higher undercooling, which gives birth to more refined grains. Hence these rates of different thermal cycles control the type and scale of the microstructures formed [273,274,275,276]. Superheated molten pool high-temperature surfaces are developed with a longer interaction time between the laser and the material. This could also result in high energy density due to low scan speed or high laser power $[272,277]$. Such conditions increase the base temperature too. This leads to the reduction of the temperature gradient and reduces the cooling rate, giving a coarser microstructure.

Contrary to this, when parameters are changed to give low laser energy density, superheating does not occur. The average temperature gradient is maintained, and an adequate cooling rate develops more refined grains. Process parameters also govern the geometry of the molten pool. A cylindrical geometry melt pool is formed due to high surface tension when the laser energy density is just enough to melt the powders. If the previous layer or substrate layer is pre-heated or partially dissolved in multiple layers, it affects the cooling rate and the microstructure [272].

In the LPBF process, there is a possibility of changes in composition and microstructures due to the vaporization of elements with high vapor pressure. This also enriches the percentage of the elements which did not evaporate by the Solute capture phenomenon [278]. The improvement in the homogenous distribution of the alloy elements is seen with high-temperature gradients. This is due to the effect known as the Marangoni effect [220]. It is also noted that the type and mode of the laser used in LPBF process also affects the microstructure development [98]. Mg powders were processed under continuous and pulsed modes [189]. When the constant wave was used, fully recrystallized grains were developed. But when the pulsed mode was used, the grains' incomplete generation was observed (Fig. 22). This is because the solidification rate was higher under pulse mode than in a continuous way. Also, there is insufficient time in pulse mode for grains to rearrange and align themselves to attain equilibrium. The average size of the grains developed under continuous mode was higher than those set under the pulse mode. Compared with other additive manufacturing methods, LPBF employs a smaller laser spot size and smaller layer thickness. This involves small melt pool formation and results in more refined grains [279]. Layer thickness alone does not influence the development of the microstructure until it is combined with the variation in other parameters. But, Salvani et al. [190] observed that the layer thickness directly influences the oxygen content and governs the microstructural changes. Further research is required to predict the microstructure of different materials under different circumstances.

\subsection*{5.1.1. Effect of process parameters on microstructures}
Cherry et al. [280] reported that the effect of LPBF process parameters significantly impacts the microstructural and physical properties of 316 L stainless steel parts. Material hardness peaked at $225 \mathrm{HV}$ at $125 \mathrm{~J} / \mathrm{mm} 3$ and was proportional to porosity, with higher porosity resulting in lower material hardness. From small ball features at low laser energy density to a mixture of both small and large ball features at high laser energy density, several types of particle coalescence resulting\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-30}

Fig. 22 - Microstructure of LPBFed part with continuous and pulsed mode laser [189].

in convex surface patterns were found. Total porosity is affected by laser energy density. Song et al. [281] investigated the effect of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V. Selective laser melting with a laser power of $110 \mathrm{~W}$ and a scanning speed of $0.4 \mathrm{~m} / \mathrm{s}$, corresponding to a continuous melting mechanism, may produce an excellent Ti6Al4V part with a high microhardness and smooth surface. The density is so great that it can be compared to bulk Ti6Al4V alloy density. Bang et al. investigated the effect of laser energy density on microstructural, mechanical properties, and chemical composition of stainless steel 316 L (SUS316L) parts fabricated by LPBF technology. Tensile characteristics declined with grain expansion as energy density rose, and light element concentrations were enhanced by accelerating dissolution.

The mechanism of destructive behavior shifted from ductile fracture to brittle fracture as light element concentrations rose, increasing hardness. Many aluminum alloys have yet to be extensively studied in terms of mechanical characteristics and microstructure because of the difficulty of manufacturing them using LPBF. The influence of laser power, hatch spacing, and scanning speed on the mechanical and microstructural properties of as-fabricated aluminum 2024 alloy (AA2024) produced by LPBF is investigated in this study. The findings show that nearly crack-free constructions with high relative density (99.9\%) and Archimedes density (99.7\%) were created. The highest microhardness (116 HV0.2) was attained with one of the lowest EDs $\left(100 \mathrm{~J} / \mathrm{mm}^{3}\right)$, which is 45 percent greater than as-cast AA2024-0 but 17 percent lower than wrought AA2024-T6 alloy [282]. The LPBF used an optimal process parameter to produce the nearly completely dense 24CrNiMo steel. The TRIP effect was used to provide excellent mechanical characteristics in 24CrNiMo steel [283].

\subsection*{5.1.2. Effect of heat treatment on microstructures}
The post-production heat treatment methods are found to be very important in terms of refining the microstructure of the part and also improving its mechanical properties. In Ti alloys, the main objective of the heat treatment process, annealing or Hot Isostatic Processing (HIP), thermomechanical processing is to transform the $\alpha^{\prime}$ martensite grains to $\alpha+\beta$ grains. Annealing and HIP are the most used processes because they are aligned in giving out the fully dense parts $[48,49,284,285]$. It has been stated that these two processes fulfill their objectives in the case of Ti alloys [49,131]. Considering such heat treatment processes, the final microstructure of the part is influenced by the relationship between temperatures, thermal cycles, and residence time [48].

One of the properties which are greatly influenced by heat treatment is micro-hardness. It is also relatively easier to test, the requirement of a small sample size being one of the reasons. Table 14 summarizes the effects of heat treatment on $\mathrm{Al}$ alloys and compares the microhardness of as-built and heattreated samples. It can be observed that some of the samples had enhanced hardness after the treatment. Softening was also observed in some of the samples, mainly those undergoing annealing treatment. The reason is mostly considered to be the reduction in density after treatment. All the cases in Table 14 are subjected to various process parameters and heat treatments such as annealing, solution heat treatment (SHT), and SHT followed by aging [154]. But all the LPBF sample values are equal to or more than those fabricated by conventional methods.

5.1.2.1. Effect of temperature. The microstructure of the LPBFproduced part has a significant say in determining its mechanical properties. And the microstructure is defined by the interface energy between the different layers, kinetic and thermodynamic factors like local stresses and wettability. The temperature has a considerable influence on microstructural formation. Various studies have been done to understand the effects of temperature on the characteristic of the alloys. For Ti alloys, it was suggested that annealing at very high temperatures gave results with excellent properties of ductility and fracture toughness [285]. Wu et al. [286] studied the effects of temperature on Ti64. The temperature range was set from 300 to $1020{ }^{\circ} \mathrm{C}$. Below the temperature of $600{ }^{\circ} \mathrm{C}$, there was almost no change from the built structure. In between the range of $750^{\circ} \mathrm{C}-990^{\circ} \mathrm{C}$, the degeneration of acicular structure started. And beyond the temperature of $10,00^{\circ} \mathrm{C}$, the original $\beta$ grains completely transformed into equiaxed $\beta$ grains. Similarly, in another experiment with Ti64, there was an increase in the volume of $\beta$ grains with increments in the temperature (Fig. 23) [48]. Before the heat treatment, prior $\beta$ grains were easily observed, but after the heat treatment, prior $\beta$ are converted and are not visible anymore. This shows an extensive growth in the grain structure. Similar results were found in another research as well $[46,175,287,288]$.

Table 14 - Effect of heat treatment on microhardness of LPBF fabricated Al alloys.

\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
Al Alloy & Heat Treatment & Details & Hardness of As-built (HV) & Hardness of Heat treated (HV) \\
\hline
AlSi7 & AA & $573 \mathrm{~K}(0.1-168 \mathrm{~h})$ & 94 & 45 \\
\hline
\multirow{6}{*}{AlSi7Mg} & T2 & $573 \mathrm{~K}(3 \mathrm{~h})$ & $124-133$ & $76-78$ \\
\hline
 & AA & $438 \mathrm{~K}(0.01-60 \mathrm{~h})$ &  & $115-150$ \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $808 \mathrm{~K}(1-8 \mathrm{~h})$ &  & $60-115$ \\
\hline
 &  & $438 \mathrm{~K}(0.01-60 \mathrm{~h})$ &  &  \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $808 \mathrm{~K}(1-8 \mathrm{~h})$ &  & $63-115$ \\
\hline
 &  & $453 \mathrm{~K}(0.01-60 \mathrm{~h})$ &  &  \\
\hline
\multirow[t]{9}{*}{AlSi10Mg} & Annealing & $573 \mathrm{~K}(2 \mathrm{~h})$ & 132 & 88 \\
\hline
 & SHT & $803 \mathrm{~K}(6 \mathrm{~h})$ & 132 & 60 \\
\hline
 & SHT & $793 \mathrm{~K}(1-4 \mathrm{~h})$ & 110 & $62-68$ \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $793 \mathrm{~K}(1 \mathrm{~h})$ & 110 & $75-79$ \\
\hline
 &  & $433 \mathrm{~K}(6-12 \mathrm{~h})$ &  &  \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $793 \mathrm{~K}(4 \mathrm{~h})$ & 110 & $94-96$ \\
\hline
 &  & $433 \mathrm{~K}(6-12 \mathrm{~h})$ &  &  \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $793 \mathrm{~K}(1 \mathrm{~h})$ & 125 & $100-103$ \\
\hline
 &  & $433 \mathrm{~K}(6-7 \mathrm{~h})$ &  &  \\
\hline
\multirow[t]{2}{*}{AlSi12} & Annealing & $573 \mathrm{~K}(3 \mathrm{~h})$ & $145-150$ & $105-115$ \\
\hline
 & Annealing & $723 \mathrm{~K}(6 \mathrm{~h})$ & 135 & 65 \\
\hline
\multirow[t]{7}{*}{AA-7075} & AA & $423 \mathrm{~K}(6 \mathrm{~h})$ & 160 & 170 \\
\hline
 & SHT & $743 \mathrm{~K}(2 \mathrm{~h})$ & 160 & 100 \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}$ & $743 \mathrm{~K}(2 \mathrm{~h})$ & 160 & 115 \\
\hline
 &  & $423 \mathrm{~K}(6 \mathrm{~h})$ &  &  \\
\hline
 & $\mathrm{SHT}+\mathrm{AA}+\mathrm{AA}$ & $743 \mathrm{~K}(1 \mathrm{~h})$ & 80 & $150-170$ \\
\hline
 &  & $383 \mathrm{~K}(14 \mathrm{~h})$ &  &  \\
\hline
 &  & $423 \mathrm{~K}(14 \mathrm{~h})$ &  &  \\
\hline
$\mathrm{Al}-\mathrm{Sc}-\mathrm{Zr}$ & AA & $573 \mathrm{~K}(0.1-168 \mathrm{~h})$ & 40 & 115 \\
\hline
$\mathrm{Al}-\mathrm{Mg}-\mathrm{Sc}-\mathrm{Zr}$ & AA & $573 \mathrm{~K}(12 \mathrm{~h})$ & $110-135$ & $160-170$ \\
\hline
$\mathrm{Al}-3.6 \mathrm{Mg}-1.18 \mathrm{Zr}$ & AA & $673 \mathrm{~K}(0.5-144 \mathrm{~h})$ & 275 & $320-410$ \\
\hline
Al-3.66Mg-1.57Zr & AA & $673 \mathrm{~K}(0.5-144 \mathrm{~h})$ & 300 & $360-420$ \\
\hline
\end{tabular}
\end{center}

It is stated that martensite decomposition is the factor responsible for balancing strength and ductility [288]. As temperature increases, ductility improves while yield strength and ultimate strength decrease. But in $\mathrm{Al}-\mathrm{Cu}-\mathrm{Mg}$ 's case, the microstructure became coarser when the heat treatment temperature was increased [289]. In this case, the ultimate tensile strength, yield strength, and elongation seem to grow in the temperature. And if there is any further temperature increase, the tensile strength decreases. This is because the distribution and size of particles do not aid in strengthening the grains anymore due to low degree of saturation after quenching [290].

\subsection*{5.1.2.2. Conduction and keyhole regimes. High-power-}
 controlled laser beams are utilized in LPBF additive metal fabrication. The depth of the molten pool is usually controlled by heat conduction in the solid substance beneath it. However, under some circumstances, the melting mechanism can shift from conduction to "keyhole-mode" laser melting. The metal's evaporation controls the molten pool's level in this phase. Melt pool depths in keyhole-mode laser melting can be substantially deeper than those seen in conduction mode. Furthermore, the collapse of the vapor cavity created by metal evaporation might leave a trail of voids in the aftermath of the laser beam [291].For LPBF, the dominating process regime, such as keyhole and conduction mode melting, was discovered as a function of line energy and intensity [292]. The aspect ratio is directly dependent on these two compound variables; therefore, the form of the resulting melt pool changes with time. Different input configurations with an energy density of $E_{V}>57 \mathrm{~J} / \mathrm{mm}^{3}$ and aspect ratios ranging from 0.35 to 1.00 were proven to create dense reliably ( $\mu(\rho)>99.95$ percent) parts, illustrating the feasibility of practical techniques in both regimes. Jadhav et al. [293] studied the LPBF processing of pure copper using a conventional infrared fiber laser. The L-PBF processing behavior is elucidated using an analytical model, which identifies conduction and keyhole regimes corresponding to the used L-PBF parameters. According to the results of the analytical model, bulk solid copper parts with near-total density are created in a keyhole regime before the commencement of keyhole-induced porosity, which is consistent with the porosity types found in the parts. When compared to the international annealed copper standard (IACS), the L-PBF manufactured copper components have an electrical conductivity of $94 \pm 1 \%$, a tensile strength of $211 \pm 4 \mathrm{MPa}$, a yield strength of $122 \pm 1 \mathrm{MPa}$, and an elongation at break of $43 \pm 3 \%$ in the as-built condition. Gargalis et al. [294] studied the processing behavior of Pure-Cu in LPBS using direct micro-calorimetry. They reported that the evolution of the keyhole melting regime and heating, melting, boiling, and vapor formation behavior while interacting with a laser beam within an LPBF environment is essential for predictable and reproducible copper deposition. Fig. 24 depicts the melt pool evolution for bare copper surfaces with increasing scanning speed at a maximum laser power of $540 \mathrm{~W}$. White arrows denote the oxides. After cross-sectioning and polishing the samples, oxide particles were discovered, concluding that

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-32}
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(a)

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(c)

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(b)

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\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-32(1)}
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(d)

Fig. 23 - The microstructure of LPBF built Ti64 on heat treatment. (a) and (b) is after sub transus heat treatment and (c) and (d) is after super transus heat treatment [48].

oxides formed during metallographic preparation. Melt pool formation was shown to be highly unstable, and when in the keyhole regime, significant swings in absorptivity values triggered explosive behavior. Melt ponds reach a depth of $400 \mathrm{~m}$, indicating deep penetration keyholing. The depth is independent of scan speed, albeit the instability of the laser-matter interaction can explain some variance in the geometry of the melt pools. Overall, it is evident that the melting regime determines a variety of unique profiles: heating, melting, and the transition from conduction to the keyhole. Chen et al. [295] reported that ex-situ sample characterization and computation thermal fluid dynamics (CtFD) modelling are used to explore melt pool shape modification as a function of preheating temperature in the conduction, transition, and keyhole regimes, as well as the underlying mechanisms in each regime as shows in Figs. 25-28. The experimental melt pool depth increases by 49 percent in the conduction regime, 34 percent in the transition regime, and 33 percent in the keyhole regime at $500{ }^{\circ} \mathrm{C}$, respectively. In contrast, the variation of melt pool width in each regime does not all follow an increasing trend but depends on the melt pool regimes. The increased heat conduction, directly related to temperature-dependent thermal characteristics, affects melt pool width variation in the conduction and transition regimes. Higher preheating temperature increases the evaporation mass, recoil pressure, and laser drilling effect in the keyhole regime, resulting in a deeper melt pool, according to confirmed CtFD simulations. Due to the higher flow rate and intense recoil pressure that accelerates the backward flow, simulations show that increasing the melt track temperature significantly lengthens the melt track length.

5.1.2.3. Residence time. Residence time is nothing but the time period for which a sample has been kept at maximum temperature during the heat treatment process. The effect of heat treatment on the microstructure of Ti64 was studied by Plaza et al. [296]. Heat treatment (annealing) was done to several samples with varying residence times; the furnace cooled them to $760{ }^{\circ} \mathrm{C}$ and then air-cooled them. On comparing samples that were annealed at the same temperature but for different residence times, it came out that finer grains and high ductility were achieved with larger residence time. Similar results were also confirmed by Vracken et al. [48]. Fig. 29 shows two heat-treated samples at $940{ }^{\circ} \mathrm{C}$ for two and $20 \mathrm{~h}$. The image shows the limited growth of $\alpha$ grains, but it gradually transforms into equiaxed ones, as indicated by arrows (Fig. 29 b). In another case of AlSi12, longer residence time gave better results for LPBF fabricated parts than those made by conventional methods such as casting [297].

5.1.2.4. Cooling rate. LPBF is very popular for fabricating parts with metallic powders, and the thermal cycles, especially cooling rates, play a huge role in controlling the microstructural behavior $[60,298,299]$. Cooling rates dictate the grain size segregation in the metallic parts [300,301]. But faster-cooling rates can restrict specific physics during the process, allowing for thermodynamically unstable phases [302,303]. And as the microstructures are monitored by cooling rate, we can say\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-33(1)}

Fig. 24 - SEM images in back-scatter mode showing the evolution of a deep melt pool for $540 \mathrm{~W}$ laser power in the transition regime from conduction to keyholing for bare copper substrates with increasing scan speed; From Figure (a) to (e) scan speed increases with intervals of $100 \mathrm{~mm} / \mathrm{s}$ and the dashed white lines show the melt pool boundaries; note the difference in scale size at the last micrograph [294].

that the part's properties are also monitored by it. Hence it is essential to ground strong relationships between the parameters and the cooling rate to develop features with good properties. Many models have been established to fund the relationship between the parameters and the cooling rates and to understand the thermal history of the LPBF process [97,304,305,306,307,308]. But as LPBF is a complex process with many interrelated parameters, it is always difficult to theoretically describe any such relations. It is stated that studying only energy input is inadequate for understanding cooling

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-33}
\end{center}

Fig. 25 - Melt pool morphology variation with preheating temperature in conduction regime [295].\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-34}

Fig. 26 - Melt pool dimensions versus preheating temperature in conduction regime ( $P=250 \mathrm{~W}$ and $\mathrm{V}=1.5 \mathrm{~m} / \mathrm{s})$ : (a) depth, (b) width, (c) aspect ratio and comparison of melt pool between experiment and simulation at the preheating temperature of (d) $100{ }^{\circ} \mathrm{C}$, (e) $300{ }^{\circ} \mathrm{C}$, and (f) $500{ }^{\circ} \mathrm{C}$ [295].

rates' effect on metallic powders. The volumetric energy density cannot predict the accurate behavior of the molten pool $[309,310]$. The same results were concluded for the experiments conducted by Pauly et al. [311] with $\mathrm{Al}-33 \mathrm{Cu}$. Apart from laser energy input, other parameters such as laser power and scanning speed are of prime importance in dictating the thermal cycle behavior in LPBF. And particularly in LPBF samples, the cooling rates at the edges seemed slightly higher than those at the center [312]. And another interesting fact is that the bottom of any LPBF sample is cooled much faster than its top part $[97,313,314,315]$. And the reason for this slow cooling rate is the accumulation of high temperatures during the process $[307,313,314]$. Also, the base plate act as a heat sink, which results in faster cooling of the base plate. The

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-34(1)}
\end{center}

Fig. 27 - Melt pool morphology variation with preheating temperature in keyhole regime [295].\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-35}

Fig. 28 - Melt pool dimensions versus preheating temperature in keyhole regime ( $P=250 \mathrm{~W}$ and $\mathrm{V}=0.5 \mathrm{~m} / \mathrm{s}$ ): (a) depth, (b) width, (c) aspect ratio and comparison of melt pool between experiment and simulation at the preheating temperature of (d) $100{ }^{\circ} \mathrm{C}$, (e) $300^{\circ} \mathrm{C}$, and (f) $500{ }^{\circ} \mathrm{C}[295]$.

cooling rate effect was studied on heat-treated LPBF fabricated Ti64 [48]. Surprisingly, it was concluded that the cooling rate's effect on the microstructures' evolution is significantly less. Owing to heat treatment above $\beta$-transus, coarse $\beta$-phase grain growth occurred. For Al-Si12, it was concluded that a cooling rate greater than $100 \mathrm{~K} / \mathrm{s}$ gave out fine microstructures. The ultrafine eutectic microstructure formed due to superheating and extremely high cooling; as a result, Si grows its most stable plan. This shows the cooling rate's effect on transforming the microstructure of the LPBFed alloys [215].

\subsection*{5.2. Mechanical properties}
\subsection*{5.2.1. Tensile strength}
The comparison between the parts fabricated by the LPBF process and the conventional method like casting for the yield strength shows that the one fabricated by LPBF has superior strength. The very reason for this is how parts are manufactured in LPBF process as rapid solidification of small amount of melted material takes place. Finer grains and microstructures are seen in the part due to the process. In alloys, too, there is negligible segregation of the alloying elements during LPBF, resulting in a much more homogenous composition and higher strength [316]. In an experiment by Wei et al. [218] for LPBF fabricated AZ91D, it was noted that the laser energy input significantly impacts the samples' tensile properties. They figured out that as the laser energy input decreases, there is also a substantial drop in the fabricated part's yield strength and ultimate tensile strength. The reason for such behavior came out to be the low-density parts due to the low supply of laser energy.

Since the LPBF fabrication mechanism relies upon the layer-by-layer addition technique, the building direction of layers also plays an important role in dictating the tensile strength of the part. In cases where laser scans were run parallel to the tensile direction, higher tensile strength was

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Fig. 29 - Microstructure of heat-treated Ti alloy at $940{ }^{\circ} \mathrm{C}$ for (a) $2 \mathrm{~h}$ and (b) $20 \mathrm{~h}$ [48].\\
observed than in those parts where scans were run in a direction perpendicular to the tensile length [317]. It was observed that LPBF specimens of $\mathrm{Br}-\mathrm{Ni}$ had shown higher tensile strength when they were fabricated and scanned in a transverse direction than those scanned in the longitudinal direction [227]. The reason was that the short vector scan which gave better results. Short scan vectors had more capacity to absorb the net energy than longer scans [318]. Another factor influencing the tensile strength is the layer thickness. Agarwala et al. [227] found that for the LPBF fabricated $\mathrm{Br}-\mathrm{Ni}$ parts, and the tensile strength rapidly rose when the layer thickness was reduced. It was shown that the tensile strength increased from $35 \mathrm{MPa}$ to approximately $60 \mathrm{MPa}$ when layer thickness varied from 500 to $200 \mu \mathrm{m}$. As discussed in the previous section, the low layer thickness aids in better bonding between the layers, increasing the part density. But too less layer thickness will be an obstacle in distributing the powder bed. Powder properties also monitor the tensile properties of the parts fabricated by LPBF. Spierings et al. [319] experimented with steel parts manufactured through LPBF with different powder granulations. They observed that powders with finer granulation though having different size distribution, gave out better tensile strength than those with coarser granulation. They concluded that more granulated powders with bigger sizes might have more pores and possibly more hollow spaces between them, affecting the bonding and, ultimately, the tensile strength. Another reason was that higher coarser powders would set up larger layer thickness. This would restrict the amount of heat penetrating through the powder bed and ultimately affect tensile strength [320]. It is also evident that introducing coarser powders in the powder mixture can improve the part's ductility. This is only valid for lower layer thickness. But they have also mentioned that if the laser energy input is more, layer thickness doesn't have any big impact as the laser penetrates deep and results in good bonding [319].

\subsection*{5.2.2. Hardness and wear resistance}
The main output from this experiment was that the high scanning speed and laser power did not influence the hardness of the part as suggested by Buchbinder et al., where the increase in scanning speed increased the hardness [322]. Table 15 shows that the optimum hardness achieved by LPBF processes AlSi10Mg when the scanning rate was varied from $1000 \mathrm{~mm} / \mathrm{s}$ to $4000 \mathrm{~mm} / \mathrm{s}$ at very high laser powers [321]. Table 15 also shows the effect of change in hatch spacings on the hardness [321]. From the table, the part's hardness is independent of the values of hatch spacings. It was also reported that the hardness achieved by AlSi10Mg fabricated by LPBF was twice that achieved when the same alloy was processed through casting. The reason for this is the rapid solidification occurring in the processing of LPBF. The same results were true for $\mathrm{Mg}$, where optimum hardness for LPBF fabricated parts was higher than that of casted parts. But in any material undergoing LPBF, the hardness usually fluctuates from the center to the periphery of the molten pool because of the differences in microstructures in those sections. But still, microhardness for the LPBF process is generally considered to be independent of the directions.

It has always been notably known that residual stresses are defects as they decrease the density and initiate cracks and pores. But Mercelis et al. [323] and Gu et al. [324] argued that if the density and cracks can be managed, and if residual stresses are retainable at a reasonable amount, they can enhance the hardness of the parts. Another reason LPBF fabricated part has good hardness properties is the rapid solidification that the part undergoes during the process. This helps in the grain refinement and development of better microstructures. Consequently, the COF (coefficient of friction) also decreases, enhancing the wear resistance of the part. Gu et al. [43] experimented on the effects of scan speed on COF and wear resistance. When a scan speed of $100 \mathrm{~mm} / \mathrm{s}$ was used to fabricate parts from $\mathrm{CP}-\mathrm{Ti}$, the COF reached a maximum level of 1.41 and increased the wear rate. Some loose debris and grooves on the surface indicated deformation and wear (Fig. 30a). When the scan speed of $200 \mathrm{~mm} / \mathrm{s}$ was used, COF was reduced, and so was the wear rate. Shallower grooves were now present with no evidence of any loose debris (Fig. 30b). An optimum scan speed of $300 \mathrm{~mm} / \mathrm{s}$ was achieved when the COF and the wear rates reached the minimum. When the scan speed of $400 \mathrm{~mm} / \mathrm{s}$ was used, plastic adherent layers were developed, which reduced the wear rate (Fig. 30c). This was also proved by Jain et al. [326]. More spalling and delamination were observed when scan speeds of more than $400 \mathrm{~mm} / \mathrm{s}$ were used (Fig. 30d). This again increased the COF and the wear rates. $\mathrm{Gu}$ et al. [43] concluded that a lower hardness value is achieved due to the intense densification and defects at low scan speed. With the formation of better microstructures, hardness increases but is still vulnerable to the micro interlayer pores at higher scan speeds. Due to a low liquid viscosity, a long liquid lifetime, and the resulting enhanced thermal stress, a combination of a low scan speed and

Table 15 - Effect on hardness due to changes in scan speeds and scan spacings.

\begin{center}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\multirow[t]{2}{*}{Scan Speed $(\mathrm{mm} / \mathrm{s})$} & \multicolumn{2}{|c|}{Hardness (HV)} & \multirow[t]{2}{*}{Scan Spacing (mm)} & \multicolumn{2}{|c|}{Hardness (HV)} \\
\hline
 & At $500 \mathrm{~W}$ & At $900 \mathrm{~W}$ &  & At $500 \mathrm{~W}$ & At $900 \mathrm{~W}$ \\
\hline
1295.5 & 144.58 & 143.91 & 0.15 & 147.56 & 148.45 \\
\hline
1698.6 & 147.57 & 148.5 & 0.200 & 146.10 & 140.44 \\
\hline
2097.4 & 148.815 & 144.57 & 0.249 & 141.299 & 146.12 \\
\hline
2493.344 & 141.39 & 148.06 & 0.30 &  & 140.46 \\
\hline
2896.37 & 144.791 & 142.06 & 0.35 &  & 145.21 \\
\hline
3290.07 &  & 149.410 &  &  &  \\
\hline
3703.16 & 147.792 &  &  &  &  \\
\hline
\end{tabular}
\end{center}

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Fig. 30 - SEM of surfaces of LPBF fabricated Ti parts at (a) $900 \mathrm{~J} / \mathrm{m}$ and $100 \mathrm{~mm} / \mathrm{s}$ (b) $450 \mathrm{~J} / \mathrm{m}$ and $200 \mathrm{~mm} / \mathrm{s}$ (c) $300 \mathrm{~J} / \mathrm{m}$ and $300 \mathrm{~mm} / \mathrm{s}$ and (d) $225 \mathrm{~J} / \mathrm{m}$ and $400 \mathrm{~mm} / \mathrm{s}$ [43].

accompanying high laser energy density resulted in the creation of microscopic balling phenomenon and interlayer thermal microcracks. On the other hand, they were using a fast scan speed resulting in a chaotic liquid solidification front and significant balling due to increased liquid instability caused by Marangoni convection.

It was shown that the laser energy density could significantly dictate the hardness of the processed part [189]. The hardness value was indirectly proportional to the energy density. As lower energy density resulted in smaller grains formation due to the high cooling rate, hardness was also influenced by the grain sizes [325]. The effect of the direction of built parts in LPBF on hardness was studied by Chelbus et al. [157] on Ti-6Al-7Nb. They demonstrated that the larger the area and the built platform and the smaller the specimen height, the more the hardness achieved. This is due to the formation of specific microstructures. This, in total, states that the part's hardness value is strongly governed by factors including thermal history, microstructures, and process parameters.

\subsection*{5.2.3. Ductility}
Ductility is usually incorporated in parts at the expense of strength. Generally, in the LPBF process, a tradeoff is encountered between ductility and strength. So, all the optimized parameters to enhance the strength of the produced part will ultimately reduce that part's ductility. Some of the main measures accounted for the strength-enhancing and simultaneously decreasing the ductility are rapid solidification, the steep temperature gradients, and faster cooling rates of small volumetric area $[48,327,328]$. The mechanism for improving ductility is well summarized in [329]. Process parameters are deeply involved in enhancing the ductility of the part [330]. The relative density and distribution of sized particles also influence ductility. The parameters which give out poor density lead to poor ductile parts.

\subsection*{5.2.4. Fatigue}
The exposure of LPBF parts to repeated cyclic stresses causes fatigue in parts, one of the most common failures. Fatigue in a LPBFed part greatly depends on the existence of any pores or

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Fig. 31 - Surface of LPBFed AlSi10Mg with powder bed temperature of $300{ }^{\circ} \mathrm{C}$, built direction of $0^{\circ}$ and peak hardened showing (a) sites for crack initiation and (b) area of forced fracture [208].

cracks. The presence of pores between the layers results in reduced dynamic strength due to the stress concentration and reduction in load-bearing capacity $[47,49,208,331,332,333]$. Investigations were taken out by Wang et al. on LPBFed FeNiCu alloy to understand the crack initiation pattern and its propagation that finally leads to fatigue failures $[332,333,334]$. They found that the area containing pores, between the layers or on the surfaces, are the crack initiation sites. The investigation also concluded that porosity is the factor that affects the fatigue properties most prominently. For better fatigue properties of LPBF parts, it is necessary to eliminate the process defects such as pores, oxides formation, and other surface defects. The pores' location and size are also of prime importance when we talk about fatigue. The pores of larger sizes, huge in numbers and located near the surface, give extremely poor fatigue strength $[49,335,336,337,338]$. Oxides and partially melted or un-melted powder particles also reduced the fatigue strength $[289,339]$. The presence of porosity and residual stresses makes it challenging to understand the effects of post-surface machining on parts. However, it has been stated that machining can help to increase fatigue strength [338].

Brandl et al. [208] investigated LPBF processed AlSi10Mg to understand the responses of temperature, heat treatment, build direction, and fatigue performance. It was observed that powders' heat treatment and heating led to better microstructure development and less cracks initiation sites. This increases ductility and fatigue resistance. However, the

building direction has the least considerable effect on fatigue properties. The heating of the powder bed reduces the cooling rate and results in fewer defects, resulting in better fatigue properties. Many studies have already established that pores on or beneath the surfaces are the main reasons for the crack initiation. This is because of the local stress generation and discontinuity in surface formation [339,340]. Figs 31 and 32 show the SEM images of the crack initiation site and area of forced fractured for the samples. Fig. 23a shows the area of forced fracture of samples that were peak hardened. Fig. 32b shows the samples which didn't go through any hardening process and had ductile fractures [208]. It was also stated that the peak hardened samples had a fracture behavior period independent of any powder bed temperature and the built direction.

In the LPBF process, the variation in the process parameters can give rise to a whole new set of properties such as grain sizes and shapes, phase composition, and microstructures to produce a tailored part. The thermal history of the part undergoing the LPBF process greatly dictates the microstructural characteristics. The thermal cycles may include high heating and cooling rates, temperature gradients, temperature rise, etc. Coarse microstructures are formed when the interaction time between the laser and the material increases or a very high laser energy density is used. The result is that it produces a superheated melt pool and increased surface temperature. This also results in a longer duration of solidification and lowers the temperature gradient and cooling rates. When parameters are adjusted to provide adequate laser energy density, the generation of a super-heated melt pool is restricted, and temperature gradients give birth to faster cooling rates. More refined grains are manufactured in such a process. But these parameters are not inclined to higher densification. The post-production heat treatment methods are found to be very important in terms of refining the microstructure of the part and also improving its mechanical properties [341].

LPBFed parts show superior yield strengths when compared to those manufactured by conventional methods. This is due to the basic LPBF process methodology wherein a very small amount of powder is melted at a time, is exposed to rapid solidification, and provides finer microstructure throughout. Many researchers have found no effect of scan

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Fig. 32 - Surface of LPBFed AlSi10Mg with powder bed temperature of $300^{\circ} \mathrm{C}$, built direction of $0^{\circ}$ and as built showing (a) sites for crack initiation and (b) area of forced fracture [208].

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spacing on the hardness of the samples. The LPBF parts have good hardness because of the rapid solidification that the part undergoes which produces refined microstructures. Also, ductility is usually achieved at the expense of tensile strength. The exposure of LPBF parts to repeated cyclic stresses causes fatigue in parts, one of the most common failures. And the fatigue life of the part is very much dependent on the existence of inevitable defects such as cracks and porosity [242].

\section*{6. Defects in LPBF process}
All the AM processes, including the LPBF process, are the modern way of manufacturing. These methods have lots of advantages, but they also come with setbacks. LPBF is no exception. The output result of the LPBF process depends on various co-related parameters, making it challenging to optimize the process and making the part prone to defects. Also, a lot of work still needs to be done to overcome all the defects and produce defect-free products through the LPBF process. Some of the significant defects influencing the result of the LPBFed parts are discussed below. Table 16 shows the defects, causes, and solution methodology [341,342].

\subsection*{6.1. Balling}
Balling is the result of typical microstructures on the surface of the LPBF fabricated parts due to the presence of loose powders in the powder bed [98]. It is nothing but the accumulation of small particles when the liquid molten phase material is broken down into miniature spheres to minimize the surface energy. Another definition states that when liquid molten metal is in poor contact with a substrate or base metal, then according to the principle of minimum surface energy, the liquid is broken down into small spheres due to surface tension. This process is nothing but a defect called balling [343]. And when a low energy density, low power, large layer thickness, and high scanning speed are provided, these spherical balls come together to form a large melt pool known as the balling region [102]. The balling region impacts the surface finish and makes it poor. It also results in many pores in the processed part and might even damage the roller, affecting the distribution of the next layer [344]. Generally, two types of balling were reported by the researchers working on defects in LPBF. The first one is the balling phenomenon with ellipsoidal balls. Their sizes are about $500 \mu \mathrm{m}$. These balls are formed when the wetting ability of the melt tracks are worsened. This results in poor bonding with the previous layer and broken melt tracks. And the wetting capabilities are further exacerbated by the low energy input or extremely severe oxidation [224]. The second type of balling is of spherical shape balls. This occurs because of the molten metal splashing, reducing its surface energy [345]. Both types of balling give rise to further defects such as cracks and porosity and hinder powder distribution in the next layer.

In general, the balling phenomenon is due to the molten metal's splashing and the molten metal's poor wettability characteristics. The literature also says that the melt pool has two different sections. The upper part is the powder in the molten liquid phase, and the lower part is the molten base or

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Fig. 33 - The phenomenon of balling with less molten substrate and more molten substrate metal.

substrate. The air-liquid or the upper part's gas-liquid interface boosts the formation of balls, and the lower part tries to restrict the upper part's tendency (Figs. 33 and 34). If the upper part's amount is less and more of the molten substrate part in the melt pool, then the upper part's tendency to cause balling can be eliminated [346]. Hence, providing a high energy density can generate more molten substrate in the melt pool, and balling can be reduced. Also, high energy density can give rise to high temperatures, reducing the liquid phase's viscosity. This would increase the fluidity and the wettability of the liquid phase further reducing balling. But if the energy density is too high, it will lead to deformation along with balling $[347,348]$. This is because of the generation of residual stresses. Excess energy also results in the vaporization of the metals. With too much energy and the sudden introduction of the gaseous phase, a very large recoil pressure is generated in the melt pool, leading to the escape of metal in the form of a jet. This jet is broken down into tiny droplets, which causes balling. Also, the un-melted powder in the vicinity of the melt pool splashes away [38].

A large number of micro-meter scale spherical balls and splashes can be seen in Fig. 35. It shows the balling defect taking place in the case of $\mathrm{Mg}-9 \% \mathrm{Al}$ powder during LPBF [191]. When a very high scanning speed was used, there was a

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Fig. 34 - Droplet splash in LPBF causing balling. sudden drop in the laser energy density, which led to unstable melt pool formation. This unstable melt pool had capillary instability, which reduced the molten phases' surface energy at small scales and the splashing of droplets of small size from the liquid surface. Furthermore, balling leads to rough surfaces due to the formation of discontinuous melt tracks. One way to tackle the balling phenomenon is to reduce the molten pool's instability by reducing the scan speed or increasing the laser power. Increasing the contact area/width or reducing the length/width ratio can stabilize the melt pool [349]. Li et al. [222] also stated that the balling phenomenon is related to oxides' formation. Hence, lowering oxygen levels, using a shielding chamber, and introducing repeated lasers to break the oxide films can reduce balling. It was experimentally stated that in LPBF processed Ni and stainless steel, the balling defect was significantly reduced by maintaining the oxygen level at $0.1 \%$. Balling was also reduced when high power and low scan speed was employed. But as $\mathrm{Mg}$ is very reactive to oxygen, maintaining oxygen level at around $0.2 \%$ could not eliminate the balling effect in Mg powders [197].

\subsection*{6.2. Porosity}
Since LPBF employs the complete melting of the metal powders, melt pools are generated, which are not stable. Further, it may develop many defects such as porosity if adequate parameters are not chosen. The pores that are developed in the LPBF process are of three types. The fusion pores, the gas pores, and the shrinkage pores [43,350,351]. Fusion pores are formed where there is insufficient laser energy density. This insufficiency leads to poor heat penetration, and the already melted and solidified layer's top layer doesn't get re-melted. This results in poor bonding with the new layers [351]. So basically, overlapping of tracks and shallow penetration are its main cause. And low laser energy density, low power, high speed, large scan space, and large layer thickness drive this defect. Such pores are strongly dominated by the process parameters and are influenced by partial or incomplete melting along the layer boundaries $[226,228,352]$. Such porosities are majorly localized in the area where partial melting is dominated.

In contrast, this porosity is minimized significantly in areas with good coherent bonding among the interlayers $[225,226,227,228,229,353]$. Fusion pores are also the result of\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-41(1)}

Fig. 35 - Balling effects due to poor selection of process parameters [191].

the gas/air trapped between the powders [354]. When these gas escapes, they form a dangerous scanning path. As the process continues, with the formation of cavities, the fluid force balances with the vapor pressure causing liquid phases to collapse and create pores $[332,355]$. Shrinkage pores are caused mainly during solidification when insufficient molten metal is produced [352]. Due to the surface tension, gas bubbles are spherical; hence, gas pores are usually spherical $[354,355]$. The gas present largely influences the gas pores in the shielding chamber incorporated partly via turbulence or when such gases cannot escape completely [356].

In a study done on LPBF processed AlSi12 for understanding the process in the presence of a shielding chamber, it was observed that more porosity defect was detected in the atmosphere of He than in the atmosphere of Ar or $\mathrm{N}_{2}$. These pores will finally be the initial site for the cracks and will result in the material's fracture $[357,358]$. Experiments on Ti64 LPBF fabricated parts denoted that porosity has disastrous effects on the part properties. The track instability and formation of pores can be minimized by adjusting the laser beam's focus $[360,361,362,363]$. Porosity has a detrimental effect on fracture properties and fatigue properties [152,359]. Heat treatment also influences the effects of porosity on part properties. Leuders et al. [49] examined LPBF fabricated Ti64 parts without heat treatment, annealing, and HIP process. It was observed that HIP reduced the porosity, and the resulting relative density was almost $100 \%$, whereas, in the as-built or the annealed part, a relative density of $99.77 \%$ was achieved. Similar results were obtained in [289].

If a layer experiences a severe balling defect, then there is a huge chance of a ripple effect, that is, the occurrence of both porosities and balling in the next layer. This leads to parts with poor properties and low density. If there is sufficient molten metal with good fluidity and if the molten pool's life is more with slow solidification, the pores can be filled, and porosity can be reduced. In the case of hydrogen, the water absorptivity is very high, and the solubility level of hydrogen in Al liquid and solid is different. Hence, to check the hydrogen porosity, the $\mathrm{Al}$ powders should be well dried up before bringing them for application. The powder bed's preheating can also help because hydrogen dissolution in the melt pool gives rise to severe porosity [364]. Pores at the top layer of LPBFed Ti6-Al-4V is shown in Fig. 36.

\subsection*{6.3. Surface roughness}
The surface finish problem has always been of great concern for parts produced via the LPBF process. It is also a big drawback in the AM process as well. Among the various AM processes, DED gives out the best part in terms of surface finish, followed by LPBF and then EBM. It was stated that the main reason causing surface roughness is the oxidation due to the

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Fig. 36 - (a) Pores at top layer of LPBFed Ti6-Al-4V, (b) magnified image of open pores and (c) magnified image of cave pores [364].\\
presence of the atmospheric gases and the adhesion of partially melted powders on the part's surface [365]. As in the LPBF process, the unused powders never leave the process area or always remain in the powder bed itself. There is still a chance of adhesion of these particles to the part surface. The average size of the roughness caused by the particles adhered on the surface is almost the same magnitude as the powder's diameter.

The small spherical balls formed in the melt pool are drawn to the melt pool's outer periphery due to the surface tension gradient inside the melt pool's flow. These balls then get solidified in the edges, resulting in surface roughness [366,367]. So, surface roughness can be reduced by reducing balling by providing high energy density, high laser power at low speed, low layer thickness, and inadequate hatch spacing. Also, large-sized particles are difficult to melt completely; hence the final part produced may come out with a lower surface finish. The other factor influencing the surface roughness in LPBFed parts is the 'stair case' effect related to the number of layers. As the layer thickness increases, the surface roughness increases too [364]. Hence, the obvious obstacle is to balance the tradeoff between the surface roughness and the built-up time of the product. Surface roughness also depends mainly on the process parameters. It is also evident that there is a reduction in surface roughness with the powder's feed rate [368]. It should be kept in mind that selecting an optimum set of parameters can only solve the surface roughness problem to a limited extent. And conventional polishing seems to be the best method to minimize surface roughness. But it was stated that parts with polished surfaces had a higher strain to failure when compared to asbuilt parts [369]. This is because mechanical work on the surface leads to stress concentration and cracks. Almost all LPBFed parts need some post-processing like simple machining for simple parts $[51,369]$ and chemical etching for complex parts [370].

\subsection*{6.4. Cracks and residual stresses}
Two major kinds of cracks can be identified in the LPBF process. These are the cold cracks and the hot cracks. The hot cracks, also called the solidification cracks, usually generate the solidification process's end-stage. Hot cracks are mainly formed due to the deformation in the solid structure of the part during solidification. Also, hot cracks can be created by insufficient convection in the liquid region. Cold cracks caused by the residual stresses are more common in the LPBF processes [371]. LPBF is accompanied by a higher cooling rate and higher temperature gradient due to its rapid melting and solidification [372]. Since the part undergoing LPBF continuously experiences thermal cycles, the solidified structures undergo rapid contraction and expansion [373]. Hence residual stresses in LPBF cannot be avoided. The strains in LPBF can mainly be categorized into structural and thermal stresses [374]. The thermal stresses in the ALM part are formed due to the uneven heating of different local zones. This results in further expansion and shrinkage in different areas near the melt pool and away from the melt pool [375]. The thermal stresses are the main reason for crack initiation in the LPBF fabricated parts [376]. Structural stresses are developed due to the volumetric expansions happening in part during the phase transformations [377]. When the residual stresses inside the part are more than the yield stress, either deformation of the part occurs or cracks are initiated to relieve the stress [378]. The cracking in the LPBF part is divided into two parts: the liquation cracking and the solidification cracking. The layer deposited experiences contraction because of the solidification shrinkages and cycles arising due to the thermal conditions. But the base/substrate's temperature or the previously solidified layer is much lower than the freshly melted layer. Hence contraction of the new layer is much more than the previously laid layer and the difference also tends to hinder the contraction of this new layer. This leads to the formation of stresses in the newly melted layer during solidification and cracking [379]. This is solidification cracking. The liquation cracking takes place in areas of partially melted powder. In these zones, rapid heating results in the melting of certain grains, especially the low melting point carbides. When the part undergoes cooling, tensile forces develop; under these forces, the melted carbides act as a site of crack initiation [380].

It was found experimentally that to reduce the residual stresses, remelting of tracks and the substrate's preheating can help. Shiomil et al. [345] found approximately $55 \%$ and $40 \%$ reduction in residual stresses due to remelting and preheating. Process parameters also effect the generation of cracks. Severe balling leads to cracks when the set of parameters is chosen to give low energy density [228]. Thermal stresses are increased at very high energy density values, which offers low viscosity and longer dwelling time of liquid phase [380]. Crack initiation takes place when the stress gradient is high. And this stress gradient is developed due to the thermal strain due to high cooling rates. Hence, rapid cooling should be avoided to overcome cracks [373].

\subsection*{6.5. Loss of alloying elements}
Volatile metals, mostly $\mathrm{Mg}, \mathrm{Zn}, \mathrm{Al}$ etc., are very prone to vaporization from the melt pool due to very high temperatures. When the laser meets the metal, these elements' high vapor pressure and low boiling points lead to their vaporization [381]. The molten pool's temperature is much higher than the elements' boiling points. The vaporization of these elements changes the composition of the part produced and alters the mechanical properties [382]. Properties that change are mainly the strength, corrosion resistance, creep, and elongation of microstructures [383]. Collur et al. [384] divided the vaporization process into three parts. It starts with transporting the elements that must be vaporized from the bulk of the melt pool to the melt pool surface. It then evaporates at the surface, and the vaporized gases travel to the surrounding spaces. In some cases, the vaporized condensates quickly back and get deposited again. This is due to the low diffusivity and these condensate metals gets remelted and covers up some of the losses [220].

Most of the vaporization takes place due to overheating of the melt pool. Hence laser energy density is an essential factor in such cases [346,385]. Vaporization leads to instability of the melt pool and changes in the composition of the deposited layers [384]. Vaporization also forms a recoil pressure within\\
the melt pool. This pressure pushes away the melt zone's liquid and leads to a defect known as the keyhole effect [301]. Vaporization also leads to a lower density of the final part as it increases porosity. Different experiments have been taken out to see the vaporization of certain elements. TiAl samples were treated under LPBF, and significant Al losses were observed [346]. LPBFed parts of Cu-4Sn had a loss of tin due to vaporization [386]. Such vaporization affects the properties and increases the instability of the melt tracks [287]. But the vaporization and the losses can be controlled. It can be minimized by monitoring the melt pool's temperature and the laser energy density [387]. But as low energy density will reduce the loss of elements, it would also trigger the part's inhomogeneity and could result in deviation of the properties from the required set of properties.

\subsection*{6.6. Oxide inclusion}
One of the inevitable defects that also deteriorates the properties of the produced part is the oxides' inclusion. If an oxide layer is present on the previous layer, it can bond with the newly deposited layer. Since the bonding is affected by the oxide layers, they also contribute to balling. Campbell stated that the alloying elements helps in introducing the oxides in the melt pool during the primary processing [388]. It has been observed that the addition of certain elements like Si or Mg to any $\mathrm{Al}$ alloy changes the nature of the oxide formed $[389,390,391,392,393,394]$. Simonelli et al. tried to track the presence of oxide films with different alloys and found that spatters and oxidation due to LPBF on alloys such as $316 \mathrm{~L}$ steel and AlSi10Mg no oxides were found on Ti6Al4V [214]. The reason is that the elements present in particular alloys have a great affinity to oxygen and lead to oxide inclusion. It has been argued that the oxide films so developed in the Ti alloys and stainless steel are thinner than the ones created on $\mathrm{Al}$ alloys. Hence, the oxide films thin in nature do not significantly impact any of the properties of LPBF fabricated parts as they get dissolved in the high-temperature melt pool or might get vaporized [207]. Many researchers are finding solutions to counter the oxidation of AL alloys because as $\mathrm{Al}$ has a great affinity to oxygen, oxides are formed even at low oxygen concentrations.

Balling is defined as the agglomeration of small metallic spherical balls. The surface tension in the liquid phase and the poor contact between the liquid metal and the substrate leads to balling formation. And these spherical balls tend to generate large melt pools due to the supply of insufficient laser energy density. Balling defects result in surface roughness and decrease the finished part's density due to the porosity it brings along with. The LPBF process's defect porosity occurs in three types: the fusion pores, gas pores, and shrinkage pores. The effect of porosity can be reduced by employing the process in a shielding chamber. AS in LPBF, the unused powders or partially melted powders remain in the process area only. They adhere to the part surface and gives rise to surface roughness. The residual stress that is more commonly observed in LPBF is the hot crack. The high cooling and heating rates make the LPBF of residual stresses inevitable. Also, the substantial temperature in the melt pool results in the vaporization of certain elements. This leads to a change in the composition of the part and alters the part's properties. The presence of an oxide layer in the pre-solidified layer hinders the new layer's distribution and inhibits the interlayer bonding. This results in poor densification. All the defects are somewhat inevitable but can be significantly minimized by selecting parameters.

\subsection*{6.7. Qualification of AM parts}
Evaluating the 'fitness-for-purpose' of fatigue-loaded parts, which is directly linked to the microstructure and flaws developed during the manufacturing process, is one of the fundamental challenges. Micro-computed tomography ( $\mu$-CT) is one of the most effective techniques for detecting faults near the surface or in thin, complex geometries. The advantages and restrictions of using statistics of extremes to analyze X-ray CT scan measurements in the context of component assessment have been discussed [393]. Romano et al. [394] reported that quality assessment of AM products is a crucial requirement, as the AM process induces internal defects that can have detrimental effects on fatigue resistance. Light microscopy on PSs and CT on three batches of fatigue specimens with various interior porosities were used to evaluate the magnitude of the most harmful defect. Both methodologies were able to locate a significant difference in the potential greatest flaw in a material volume corresponding to a specimen's gauge section, according to the findings [395]. Using synchrotron tomography techniques, Bao et al. [396] examined the defect evolution during high-temperature tension-tension fatigue of LPBF AISi10Mg alloy. This study used time-lapse synchrotron radiation X-ray micro-computed tomography (SR-CT) to track damage accumulation for AlSi10Mg test-pieces fabricated by LPBF over their entire fatigue lives under tension-tension cyclic loading at $250^{\circ} \mathrm{C}$ in situ (ranging from 180 to 38,000 cycles). Under combined-cycle fatigue circumstances, Patriarca et al. [397] provided a probabilistic approach for defining design stress and allowable faults. The data were utilized to determine the average and variation of the materials parameters, which were then used to feed Montecarlo simulations and determine the design stress based on a target likelihood of failure. This research focuses on determining safety margins that are only dependent on the inherent variability of the elements that influence the accumulation of damage in mechanical components.

The specific attributes of SR-CT were provided by Wu et al. for the investigation of fatigue characteristics in structural engineering materials. Non-destructively, there is a lot of knowledge about fracture and fatigue in both 3D and $4 \mathrm{D}$. For environmental and mechanical loads allow for 3D imaging of damage buildup and time-lapse imaging of damage evolution [398]. Hu et al. [399] studied the impacts of fabrication defects on the fatigue behavior of Ti-6Al-4V structures that were selectively laser melted. In terms of population, morphology, dimension, and position, X-ray micro-computed tomography (CT) is utilized to quantify the porosity and lack of fusion flaws. To forecast the anticipated fatigue life, the defect size and location are integrated with the NASA/FLACGRO (NASGRO) fatigue crack growth model, in which an effective beginning crack length is determined using the cyclic plastic zone and defect radius. X-ray\\
computed tomography (CT) has been used to study the effect of metallurgical defects that critically influence the anisotropic fatigue resistance of additively manufactured parts under cyclic loading [400]. Based on X-ray CT data, extreme value statistics were utilized to forecast the anticipated defect population in the critical near-surface region of fatigue samples. The elevated temp cyclic loading governed low cycle fatigue experiments of a laser powder bead fused AlSi10Mg alloy were investigated by Bao et al. [401]. Following stress relief $\left(2 \mathrm{~h}\right.$ at $\left.300{ }^{\circ} \mathrm{C}\right)$, three temperatures $\left(100^{\circ} \mathrm{C}, 250^{\circ} \mathrm{C}\right.$, and $400^{\circ} \mathrm{C}$ ) and two cyclic loading waveforms (standard triangular and dwell-type trapezoidal waveforms) were used to investigate both the mechanical response and related microstructural changes of this additively manufactured (AM) aluminum alloy. The bulk mechanical reactions were found to soften in a cyclic pattern, with less stress relaxation and less energy wasted per cycle.

Zhang et al. [402] studied the high-cycle and very-highcycle fatigue lifetime prediction of additively manufactured AlSi10Mg via the crystal plasticity finite element method. The fatigue life of the sample with defects is much lower than that without defects. $0^{\circ}$ specimens have a better fatigue performance than $90^{\circ}$ ones. This work is beneficial in determining the fatigue lifetime and helps to improve the fatigue behavior of AlSi10Mg. The population, morphology, and dimensions of porosity, as well as the lack of fusion defects, were all quantified using high-resolution synchrotron radiation X-ray computed tomography (CT). The larger-sized flaws in the LPBF product alloy are more essential for crack initiation than the grains, resulting in low fatigue resistance and a wide variation in fatigue life. Using a combination of statistics of extremes and the Murakami model, the fatigue strength was then evaluated in terms of the defect population [403]. In situ X-ray imaging was used to assess fatigue fracture propagation from various flaws in an additively made AlSi10Mg alloy [404]. The crack propagation phase was estimated to account for $35-60 \%$ of overall fatigue life, with a greater proportion at high stress amplitudes. Ravi et al. studied the crack closure mechanism of an additively generated, naturally occurring, tortuous, 3D microstructurally small fatigue crack (SFC) in Inconel 718. Insitu non-destructive characterization employing high-energy $\mathrm{X}$-ray diffraction techniques is used to track the evolution of the 3D microstructure and micromechanical response in the crack front region. Based on the stress state of twelve grains examined at the crack tip, the crack closure events of the SFC front were found to be spatially diverse in terms of loading progression driven by the local stress state of the grains [405]. Choo et al. [406] used in-situ synchrotron x-ray computed microtomography study methodologies to investigate the deformation and fracture behavior of a laser powder bed fusion produced stainless steel. Using high-resolution synchrotron x-ray computed microtomography, the tensile plastic deformation and fracture behavior of a laser powder bed fusion treated $316 \mathrm{~L}$ stainless steel alloy were examined in situ (sXCT).

Hu et al. investigated the effect of in-situ micro-rolling on the strength and ductility of the part fabricated by wire arc hybrid additive manufacturing [407]. The WAAM processed part comprised higher defects, whereas, after micro-rolling, the defects were removed and microstructure also improved, improving the mechanical properties of parts. Nezhadfar et al. [408] studied the structural integrity and effects of build orientation on microstructure, porosity, and fatigue behavior of additively manufactured aluminum alloys. Xie et al. [409] studied the effect of rolling on the WAAM processed $\mathrm{Al}-\mathrm{Mg}-\mathrm{Mn}$ alloy. The results show that advanced HRAM can result in significant grain refinement and enhance the material failure resistance compared with standard WAAM.

\section*{7. Numerical modelling, optimization, and machine learning techniques}
\subsection*{7.1. Governing equation for heat transfer analysis}
Shiva et al. [410] elaborated for heat transfer analysis; generally, heat input is taken under the Gaussian distribution, and the losses due to convection and radiation are also accounted for. The transient thermal analysis must determine the temperature history at each specific point of the deposited material over the substrate and across the powder bed. The heat conduction governing equation in three dimensions is given as

$k_{m}\left(\frac{\partial^{2} T}{\partial x^{2}}+\frac{\partial^{2} T}{\partial y^{2}}+\frac{\partial^{2} T}{\partial z^{2}}\right)=\rho C_{p}\left(\frac{\partial T}{\partial t}-V \frac{\partial T}{\partial y}\right)$

where $\mathrm{V}$ is the velocity vector $\left(\mathrm{m} \mathrm{s}^{-1}\right), \rho$ is the density of the material $\left(\mathrm{kg} \mathrm{m}^{-1}\right), C_{p}$ represents the specific heat $\left(\mathrm{kg}^{-1} \mathrm{~K}^{-1}\right), \mathrm{T}$ is the temperature $(\mathrm{K}), k_{m}$ represents the thermal conductivity $\left(\mathrm{W} \mathrm{m}^{-1} \mathrm{~K}^{-1}\right)$. The thermal conductivity which will compensate the flow of fluid in the molten metal is given as

$k_{m}=\left\{\begin{array}{l}k_{o}, \mathrm{~T}<\mathrm{T}_{m} \\ k_{0}+k^{\prime}, \mathrm{T} \geq \mathrm{T}_{m}\end{array}\right.$

where $T_{m}$ represents the melting point of the material, $k^{\prime}$ is used in the thermal model as the additional value for the convection heat transfer capability and $k_{0}$ represents the thermal conductivity of the materials used. The natural boundary condition is mathematically represented as

$k_{n} \frac{\partial \mathrm{T}}{\partial n}-q+h\left(\mathrm{~T}-\mathrm{T}_{0}\right)+\sigma \varepsilon\left(\mathrm{T}^{4}-\mathrm{T}_{0}{ }^{4}\right)=0$

where $\sigma$ represents the Stefan-Boltzmann constant, $\varepsilon$ represents the emissivity, $k_{n}$ is thermal conductivity ( $\mathrm{W} \mathrm{m}^{-1} \mathrm{~K}^{-1}$ ) normal to the surface, $q$ is the imposed heat flux onto the surface (W), $h$ is the convection heat transfer coefficient $\left(\mathrm{Wm}^{-2} \mathrm{~K}^{-1}\right)$ and $\mathrm{T}_{\mathrm{o}}$ is the ambient temperature $(\mathrm{K})$. The initial condition is set to be $t=0$ for the analysis of transient heat transfer as

$T(x, y, z, 0)=T_{0}$

where $T_{0}$ represents the initial temperature (K).

\subsection*{7.2. Governing equation of mechanical analysis}
The mechanical analysis is usually carried out by a method including the incremental variation of stress and strain. In cartesian coordinates, the relation for the strain-displacement is given as\\
$\varepsilon_{x}=\frac{\partial u}{\partial x}, \varepsilon_{y}=\frac{\partial u}{\partial y}, \varepsilon_{z}=\frac{\partial w}{\partial z}$

$\gamma_{x y}=\frac{\partial u}{\partial y}+\frac{\partial v}{\partial x}, \gamma_{y z}=\frac{\partial v}{\partial z}+\frac{\partial w}{\partial y}, \gamma_{z x}=\frac{\partial w}{\partial x}+\frac{\partial u}{\partial z}$

where $\mathrm{u}, \mathrm{v}$ and $\mathrm{w}$ represent the displacements along the $\mathrm{x}, \mathrm{y}$ and $\mathrm{z}$ direction. $\varepsilon_{\mathrm{x}}, \varepsilon_{\mathrm{y}}$ and $\varepsilon_{\mathrm{z}}$ represents the normal strains in $\mathrm{x}$, $y$ and $z$ directions respectively and $\gamma_{x y}, \gamma_{y z}$ and $\gamma_{z x}$ represents the shear strain in $\mathrm{xy}, \mathrm{yz}$, and $\mathrm{zx}$ planes respectively. The thermal strains are considered to be equal in three directions considering isotropic material. The sum of the incremental plastic strain, incremental thermal strain and incremental elastic strain gives the increment of the total strain, which is represented as

$\{d \varepsilon\}=\left\{d \varepsilon^{t}\right\}+\left\{d \varepsilon^{p}\right\}+\left\{d \varepsilon^{e}\right\}$

The incremental stress can be mathematically represented according to the Prandtl-Reuss flow rule and Von-Mises yield criteria as,

$\{\mathrm{d} \sigma\}=\left[\mathrm{D}_{e p}\right]\{\mathrm{d} \varepsilon\}-\left[\mathrm{D}^{e}\right]\{\alpha\}(\Delta \mathrm{T})$

where

$\left[D_{e p}\right]=\left(\left[D^{e}\right]-\left[D^{e}\right]\left\{\frac{\partial f}{\partial \sigma}\right\}\left\{\frac{\partial f}{\partial \sigma}\right\}^{T}\left[D^{\varepsilon}\right] \frac{1}{3 G+E_{T}}\right)$

where $\left[D^{e}\right]$ is the elasticity matrix consisting of mechanical properties like Poisson's ratio $\mu$ and Young's Modulus E (GPa), $E_{T}$ is the local slope between stress and plastic strain of the particular material, G represents the shear modulus (GPa) and $\alpha$ is the thermal expansion. The first part of the equation of $\left[D_{e p}\right]$ is due to the elastic response of the material or recovery of elastic response when the materials in the plastic zone. The second term of the equation is due to the plastic flow of material, which is zero when the material is elastic zone only. The mechanical boundary conditions in modeling and real practical cases are very similar.

\subsection*{7.3. Governing equation for thermo fluid analysis}
According to Mishra and Kumar [411], The governing equation of the analysis take into account the mass, momentum and energy transport in the powder and in the solid domain. The equation to represent the mass transport is

$\nabla \mathrm{u}=0$

where $u$ represents the molten metal flow velocity vector. All the energy that is given in the form of the laser is consumed in heating the metal and melting it and is also helpful in compensating the heat losses to the surroundings. The equation for the energy transport is

$\frac{\partial\left(\rho C_{p} T\right)}{\partial t}+u . \nabla\left(\rho C_{p} T\right)=\nabla .(k \nabla T)+q$

where $\rho$ is the mixture density, $T$ is the temperature, $C_{p}$ is the specific heat capacity at constant pressure, $\mathrm{t}$ is time and $\mathrm{k}$ is the thermal conductivity. The quantities $\rho, C_{p}$ and $k$ are functions of powder layer and liquid metal properties. These properties are functions of solid and liquid metal properties in stages of solidification. During melting in the powder layer, the values of $\rho, C_{p}$, and $k$ are determined by averaging the values in the liquid and powder phase.

$\rho=\left(1-f_{\mathrm{L}}\right) \rho_{p}+f_{\mathrm{L}} \rho_{\mathrm{L}}$

$k=\left(1-f_{\mathrm{L}}\right) k_{p}+f_{\mathrm{L}} k_{\mathrm{L}}$

$C_{p}=\frac{1}{\rho}\left\{\left(1-f_{L}\right) \rho_{p} C_{p S}+f_{L} \rho_{L} C_{p L}\right\}+\frac{L \partial \alpha_{m}}{\partial T}$

In above equations, $f_{L}$ is the liquid fraction, $\rho_{L}$ is liquid metal density, $k_{\mathrm{L}}$ is thermal conductivity and $C_{p L}$ is the specific heat capacity of liquid metal. $\rho_{p}$ is the powder layer density and $k_{p}$ is the powder thermal conductivity. The eq. (15) approximates the effective specific heat capacity of the powder layer considering the solid-liquid phase change. In Eq. (15), $C_{p s}$ is used as the specific heat of solid powder and is usually considered the same as that of bulk solid. The quantity L is the latent heat of phase change, and $\alpha_{m}$ is the mass fraction given as

$\alpha_{m}=\frac{1}{2} \frac{\left(f_{L} \rho_{L}-\left(1-f_{L}\right) \rho_{p}\right)}{\left(f_{L} \rho_{L}+\left(1-f_{L}\right) \rho_{p}\right)}$

The liquid fraction $f_{L}$ is calculated as

$f_{L}= \begin{cases}0 & T<T_{S} \\ \frac{T-T_{S}}{} & T_{S} \leq T \leq T_{L} \\ T_{L}-T_{S} & T>T_{L} \\ 1 & \end{cases}$

In Eq. (17), $T_{S}$ and $T_{L}$ are solidus and liquidus temperatures of the material. In the solid domain as well as the solidifying material, the values of $\rho, \mathrm{cp}$, and $\mathrm{k}$ to be used in Eq. (12) are determined using same Eq. (13)-(15), except for using $\rho_{S}$ and $k_{S}$ in place of $\rho_{\mathrm{P}}$ and $k_{\mathrm{P}}$.

\subsection*{7.4. Modelling of laser energy}
LPBF process typically uses laser as a heat source to melt the powders. It has been also noted that the laser spot has the maximum intensity at its center and it gradually decreases towards the periphery. The laser energy hence is modelled generally according to the Gaussian distribution and its equation is given as:

$q=\frac{2 A P}{\pi R^{2}} \exp \left(\frac{-2 r^{2}}{R^{2}}\right)$

where $\mathrm{P}$ is laser power, $\mathrm{r}$ is radial distance from a point on powder bed surface to the center of laser spot, $R$ is the effective radius, A is laser energy absorptance of material.

\subsection*{7.5. Numerical modelling of LPBF process}
AM has brought a change in how things were done from the beginning of designing and getting the parts to life and in fields of innovations. AM has revolutionized many industries by decreasing the cost and manufacturing complex components $[1,2,7]$. In the design step, the designers are accompanied by the latest software available in the market. The\\
software enables one to predict the characteristics and performance of a part built with specific parameters. It helps in reducing cost and time. But such technology needs a detailed understanding of all the physics happening during the physical printing of any part. And the main obstacle is understanding the relationship between the parameters and the output properties, which varies from material to material [24,25]. LPBF uses a layer-by-layer approach to build a part and where powders are melted to form the $3 \mathrm{D}$ part. It is now certain to say that such complex processes can never be fully defects-free. The manufacturers' challenge is to make a product with minimum defects, including optimizing the process parameters. Researchers are developing new ways to optimize the process parameters, such as numerical modeling. The iterations for optimizing the process parameters by practically manufacturing a dozen parts and improving it stepwise take a lot of time and resources. The main advantage of numerical modeling is that it does not need any physical product fabrication to study. Hence, saving time, raw materials, and cost $[412,413]$. One of the essential models is the thermomechanical model, where all the thermal history and the residual stresses are taken into account. It has been known that experiment-based optimization is necessary, but numerical modeling gives a steady start to the researchers for manufacturing a part by any complex process. They have an insight into the process at an early stage and can now optimize the process to give out the best results [414].

The governing equations mentioned in the previous section are the basic mathematical background for any simulation work. Researchers have opted for other methods to simulate the LPBF process accordingly to their objective. Any model to give an appropriate result must contain factors related to all the thermal and temperature behavior of the process. Conti et al. [415] developed thermomechanical modelling of the growth process using a finite element that studies the changes in the part's behavior during the phase changes of the material from powder to liquid to solid. The study of the effect of scan strategy on the residual stresses was done by formulating thermomechanical modelling [31]. It has been noted that stress development and its distribution vary with different scan strategies. Hussein et al. [416] studied the stress and temperature field in LPBF fabricated 316L Steel using a nonlinear transient thermomechanical model. The study was done using the finite element method and for parts showing high-temperature gradients and thermal stress formation. They used ANSYS software to establish a non-linear transient finite element model for studying the various parameters associated with LPBF. The general governing equation used for the thermal analysis mentioned in the previous section (eq. (2)) is used in this paper. The same is used in the research reported by Hu et al. [307]. It has been stated that a single source melts the powders in the LPBF process, only that is the laser beam $[417,418]$.

The primary modes of transfer of heat from a laser to a material and then to other parts of the material are radiation, convection, and conduction. And all these modes are well incorporated in eq. (2), which serves as a good model for thermal analysis. Gusarov et al. [217] establish a model combining the effect of heat transfer and radiation on the balling defect with different scan speeds. Baere et al. [419] developed a CFD model to study the temperature profile and the melt pool's size during the LPBF process. Leitz et al. [420] used a Thermo fluid multi-phase dynamical model for the LPBF process. This model deals with the basic fluid flow in the molten metal and heat transfer during the process. The same model was also used to understand the effects of powder characteristics of molybdenum in the LPBF process [421]. The results gave a better understanding of the powder's properties, powders distribution, and their effects on the LPBF process. For the LPBF of Ti6Al4V, a coupled thermo-metallurgicalmechanical model was established to understand the temperature cycles and phase changes in the multi-layer LPBF process. The thermal analysis of LPBF is based on heat conduction and heat source with the absorption of heat and powder bed scattering [341].

The modelling of the heat source is also very important. As studied by various researchers, heat input in the LPBF process is one of the main parameters influencing the output product's characteristics. Gaussian heat distribution is commonly used as a laser source in the simulation and modelling for LPBF as in [55,242]. However, some alternations are noticed in different research according to the parameters and objectives. Tan et al. [422] used a laser beam in the LPBF was assumed to be asymmetric concerning Gaussian distribution. Also, a moving point Gaussian laser scan was modeled to understand the temperature distribution in a solid model undergoing LPBF [423]. In the research work of [400] and many others, the laser source is modeled as per Gaussian distribution. In LPBF, laser energy is transmitted by a specific area called the laser spot, and the modelling is done to replicate the heat intensity in the center and the laser's periphery.

Luo et al. [424] stated that most of the modelling work that has been done on LPBF uses a moving Gaussian heat source to model the melt pool profile, and it is evident that such a model takes a lot of time and computational cost and cannot be used for the lower level. To reduce the computational time and cost, a line heat source is proposed to accelerate heat transfer simulation in the LPBF process by increasing the time step and reducing the number of cells. The line heat source replaces the moving laser source. The simulation results show that the replacement does not have any more significant effects on the development but can reduce the computational time by a considerable margin.

FEM has always been a very approachable method for researchers for numerical modeling and simulation. Many researchers have opted FEM for the simulation and study of LPBF too. Schanzel et al. [425] describe a nonlinear macroscale FEM simulation method to predict the detailed temperature history of the material and the residual stresses. In [414], the island strategy is modelled to reduce residual stresses and deformation generation. Majeed et al. [423] proposed a FE model to understand layers' behavior on different underlying surfaces due to varying process parameters. Hu et al. [307] developed a model using FE to simulate the deposition of multiple layers of AlSi10Mg in the LPBF process. The FE model helped investigate crucial physics such as temperature distribution, cooling rates, morphology, melt pool dimensions, etc. The investigation was conducted for multi-layer parts using finite elements, and also the experiments on melt pool formations for single tracks were carried out using FE [337]. Li\\
et al. [426] developed a FE model to study the temperature profile and the residual stress fields in LPBF. The temperature profile is obtained by independently conducting the heat transfer analysis, and the temperature history is taken as a temperature load for the subsequent investigation step. Another model based on FE was developed to simulate thermal behavior on the melt pool [427]. The effects of the process parameters like the scan speed and laser power were investigated on LPBFed parts of AlSi10Mg using a finite element method [55].

A lot of research has been going on to optimize the LPBF process through modelling and simulation. But due to the intense process complexity, there are always some gaps in the results. Many researchers are now trying different simulation approaches to increase accuracy as far as possible. Olleak et al. [428] proposed a system that includes remeshing. The remeshing technique drastically reduces the computational time with almost no change in results. The LPBF of powders was simulated in software by developing a grid model to transfer heat in porous material [429]. This grid technique used contact surfaces between the particles to study heat conductivity. Ahmad et al. [430] developed the contour mode and inherent strain-based numerical model to study the stress concentration in the LPBFed part. Ti-alloys and Inconel were also analyzed for different parameters such as cooling rates and temperature gradient based on the same material. Scanning strategy is also believed to be an important and decisive factor in contributing to the final properties of the LPBFed product. Jhabvala et al. [431] used techniques to eliminate drawbacks, exploit the scanning strategy's benefits, and detect the part's heat-affected zones.

The simulation of any process gives results based on computational figures only. It completely ignores the practical obstructions or variations that might occur during the practical execution of the process. Hence, validating the simulation results with the result of an experimental set is very important. Mohanty et al. [432] used a 3D finite volume alternating directional implicit numerical technique to simulate the single-track scanning. The parameters used in the model were calibrated with the experiment of a single layer scan of Ti alloys. The results obtained in the simulation of [400] were also compared to experimental paper results. The results were validated that the melt pool's temperature, HAZ, and dimensions increased with the number of layers. Hu et al. [307] compared the simulation results of the melt pool dimension with the experimental values. There was a considerable resemblance in both the results, especially concerning the melt pool shape. It was also seen that melt pool depth was more well-matched with the experimental values, but the melt pool length was not. Any deviation from the experimental value indicates further improvement of the simulations. Dai et al. [314] studied the effect of linear energy density on the densification and melt pool dimensions of the LPBFed part. And the results for the calculation of the relative density was compared to that of experimental work. And it was found that the resemblance in the results was pretty good too. Loh et al. [433] used different laser powers and scan speeds on $\mathrm{Al}$ alloy undergoing LPBF. A very effective model was established even to account for the shrinkages and vaporization, and the results were then validated. In some cases, the chronology of the validation process may be reversed too. Ho et al. [434] experimentally investigated the fabrication of the LPBF part of the rectangular airflow channel and found it has a higher efficiency than the circular one. Then the numerical studies were done to affirm the results of the case.

One of the main obstacles in LPBF is understanding the relationship between processing parameters and final part properties. Hence, researchers have developed process simulation as iterations for optimizing process parameters. The governing equations are the mathematical background for any simulation work. Different objectives may call upon the usage of different model. A model must consider the dependency of the process on temperature. Thermo-mechanical and thermo-fluid models are popular among researchers due to the similarity in the practical world. The modelling of heat sources is also of prime importance. It is noted that the laser's maximum energy intensity is focused on the center of the laser, and it gradually decreases along the periphery of the laser spot. Hence, Gaussian distribution models the heat source with maximum intensity at the center. The solutions for most of the models are extracted using FEM. Multiple researchers have opted FEM as the primary method to solve the thermal equations. But any simulation gives a result based on computational figures only; hence, it is essential to validate the simulation results with an experimental result.

\subsection*{7.6. Machine learning methods for predicting LPBF process characteristics}
Because of its capacity to make complicated geometric parts, LPBF is the most prevalent metal additive manufacturing process. It has a large body of academic research and industrial investment behind it. Despite the extensive numerical simulation of LPBF using finite element analysis, process monitoring is still required to assure dependable part manufacturing and reduce post-build quality evaluations. Artificial intelligence-based machine learning and deep learning techniques are needed to make the LPBF process efficient. Gaikwad et al. [435] used height map-derived quality measures for single tracks and pyrometer and high-speed video camera data acquired under various laser power and laser velocity settings to construct and test machine learningbased predictive models. Sequential Decision Analysis Neural Network (SeDANN) a scientific machine learning model, was used to predict the melt pool dynamics and post-build quality measurements. Fig. 37 shows the methodology to implement the machine learning-based predictive models. Singh et al. [436] reported that machine learning (ML) could resolve this barrier by leveraging datasets obtained at various phases of the LPBF process chain. The incorporation of ML into the multiple stages of the LPBF process chain, which could lead to enhanced quality control, is investigated in this viewpoint paper. ML can be used for part design and file preparation before L-PBF. Then, machine learning techniques may be used to optimize process parameters and monitor in real-time. Finally, machine learning can be included into postprocessing. Okaro et al. [437] proposed applying ML-system to predict flaws/defects in AM products automatically. A semi-supervised learning approach was used, which can use

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-48}
\end{center}

Input meltpool features

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-48(2)}
\end{center}

Output predicted laser parameters

Output predicted mean and standard deviation of width\\
\includegraphics[max width=\textwidth, center]{2024_04_13_5cb034414d6b3ef85164g-48(1)}

Output predicted percent continuity

Network input

Network output

Fig. 37 - A schematic of the sequential decision analysis neural network (SeDANN). The sensor data and height map shown above belong to a single-track deposited at linear energy density (EL) of 0.33, i.e. balling regime. The statistical probability distribution features extracted from the pyrometer are used in the first echelon artificial neural network (ANN) to predict the laser process parameters ( $P$ and $V$ ) followed by melt pool features derived from the high-speed video camera to predict the mean width and standard deviation and single-track continuity at higher echelons [435].

data from both builds where the resulting components were certified and build where the quality of the resulting components was uncertain during training. This makes the method cost-effective, especially when part certification is expensive and time-consuming

Ogoke et al. [438] proposed deep reinforcement learning for predicting thermal characteristics and minimizing the likelihood defects of the LPBF process, as shown in Fig. 38. During the melting process, the developed control algorithm changes the laser's velocity or power to assure melt pool consistency and minimize overheating in the formed product. The control algorithm is trained and verified using accurate simulations of the powder bed layer's continuum temperature distribution under varied laser paths.

Baumgartl et al. [439] proposed an integrated model combination of neural network deep learning-based model and thermographic off-axis imaging as a data source to predict printing defects and process monitoring such as melt pool or off-axis infrared monitoring. The proposed methodology is $96.80 \%$ accurate in predicting delamination and splatter. Moreover, the model is very small with low computing cost and suitable to operate in real-time even on less powerful hardware. Most defects such as keyholing, porosity, and balling occurred at the size and timescales of the melt pool itself. Monitoring of such defects is critical. Scime and Beuth [440] proposed a deep learning approach that proposed the possibility of in-situ detection of such important defects. The morphology of LPBF melt pools in the Inconel 718 material system is studied using a high-speed visible-light camera with a fixed field of view. Computer Vision techniques are utilized to create a scale-invariant description of melt pool shape, and unsupervised Machine Learning is used to distinguish between observed melt pools. In-situ signatures can be found by observing melt pools created across process space, which may reveal faults similar to those seen ex-situ. The application of supervised Machine Learning to categorize melts pools observed (with the high-speed camera) during the fusing of non-bulk geometries such as overhangs was facilitated by this coupling of ex-situ and in-situ morphology.

Sanchez et al. [441] utilized the potential of ML to establish a relationship between process, structure, and properties to predict the creep rate of 78-alloy-based parts produced by LPBF

\begin{center}
\includegraphics[max width=\textwidth]{2024_04_13_5cb034414d6b3ef85164g-49}
\end{center}

Fig. 38 - The Deep Reinforcement Learning framework. (a) In Reinforcement Learning, an agent selects an action, based on the current state $s$ and the policy $\pi$ mapping each state to an action. The action is deployed in the environment, influencing the environment, modifying its state, and producing a reward based on this influence. The policy is updated based on reward, aiming to maximize the cumulative reward. (b) The state is represented by cross sections of the domain in the $x-y$, $y-z$, and $x-z$ planes near the location of the laser, for the three previous timesteps of the simulation. (c) The policy network is a fully connected neural network that takes in the state's current representation and predicts an action to maximize the expected reward. The Policy Network is implemented as a two-layer multi-layer perceptron with hyperbolic tangent activation functions and 64 neurons per hidden layer [438].

process. The ML algorithm was trained using input data that included LPBF process parameters and geometrical characteristics (porosity images) of materials obtained from the image analysis techniques. The model significantly and accurately predicts the minimum creep rate of LPBF with a high $98.60 \%$. Zhang et al. [442] developed a hybrid machine learning model to predict the manufacturability assessment of the LPBF process. For the design aspect, a voxel-based convolutional neural network (CNN) model was used, and for the process aspect, a neural network (NN) model was used. After that, the two models are integrated to forecast the architecture's manufacturability under the chosen LPBF process parameters. The validation samples were randomly selected, and the findings confirmed that the proposed model can properly forecast the design's manufacturability. However, the computational capacity and amount of training datasets of the proposed model are limited, necessitating future research in this area. Liu et al. [443] developed a machine-learning algorithm based on Gaussian process regression to predict the optimal condition of LPBD for manufacturability of fully dense AlSi10Mg samples with high strength and ductility.

Peng et al. [444] used the capability of ML to predict the fatigue life of LPBF processed AlSi10Mg alloy. An Extreme Gradient Boosting model was found to be capable of accurately predicting fatigue lives. The importance of these variables in limiting fatigue life is rated in the order provided above. The model predicted varied sample lifespan, implying that microstructure played a modest role. When testing parallel to the construction direction, it was discovered that the vast projected area of the flaws on the fracture plane was principally responsible for the reduced lifetimes observed. The more generic two-variable Murakami model adequately predicted fatigue lifetimes, and an empirical model for which the ML model validated empirical dependences was even more nearly expected. Zhang et al. [445] used a neuro-fuzzy-based machine learning method for predicting the high cycle fatigue life of LPBF processed stainless steel 316 L. A training dataset containing fatigue life data for samples subjected to varied processing conditions, post-processing treatments, and cyclic loads was created to simulate a complex nonlinear input-output environment. The associated fracture mechanisms were investigated, such as crack initiation and deformation modes. The training data was used to create two models that used the processing/post-processing parameters and the static tensile characteristics as inputs. Despite the wide range of fatigue and fracture parameters, the models showed good prediction accuracy compared to test data, and the computationally produced fuzzy rules match the fracture mechanisms well. Bao et al. [446] utilized the potential of the ML technique to determine the effect of defect location, size, and morphology on the fatigue-performance of LPBF processed components. The critical and important defects responsible for high-cycle fatigue failure were identified using the characterization technique and used as input data for training using a support vector machine (SVM). The grid search strategy with testing data was chosen for fitting the model\\
parameters to speed up the optimization process. The coefficient of determination between predicted and experimental fatigue life is found to be as high as 0.99 , showing that the SVM model has good training ability. Moon et al. [447] established a relationship between surface roughness and pore characteristics on fatigue performance of Ti-6Al-4V alloy-based samples. The data were further used for training a machine learning model for the prediction of the fatigue life of components. To build a relationship between surface and pore features and fatigue data $(\log N)$, a drop-out neural network (DONN) was used, and good prediction accuracy was exhibited. Hassanin et al. [448] proposed a deep learning neural network (DLNN) model to rationalize and predict the densification and hardness of LPBF processed Ti-6Al-2Sn-4Zr-6Mo alloy. A relationship between the process parameters and output characteristics has been developed and used as a input data to train the DLNN model. The model that was created was validated and utilized to create process maps. The trained deep learning neural network model had the highest accuracy with a mean percentage error of $3 \%$ and 0.2 percent for porosity and hardness, respectively. Deep learning neural networks were found to be an effective technique for predicting material qualities from tiny data sets, according to the findings.

\section*{8. Summary}
Additive manufacturing is a ubiquitous topic in the industrial and academic fields. This review deals with the understanding and recent up-gradation of the LPBF process. LPBF became a versatile method applicable to many metals and their alloys, and hence it is receiving significant attention. A complete review of the LPBF process is done, and some key points have emerged, which are of prime importance. The significance of various process parameters is also dealt with to minimize the defects in the final product.

\begin{itemize}
  \item For the LPBF process application on any material and to achieve the highest possible density and the required mechanical properties on the LPBFed part, the most important thing is to precisely monitor the processing parameters.
  \item The exposure of metals/alloys to the LPBF process has sorted out many problems attached to the traditional manufacturing processes. The properties inherited by the LPBFed sample show that LPBF can produce samples with properties superior to those produced by the conventional methods.
  \item The densification behavior of metals is primarily influenced by the variation of laser energy density controlled and altered by several other process parameters. The densification can be directly related to the change in laser energy density due to the variation of process parameters.
  \item In LPBF, powder particle size and distribution's effect is considered less important because all particles undergo complete melting. Unlike SLS, where partial melting occurs, powder parameters have a negligible contribution to the part's densification.
  \item The microstructural characteristics of the parts processed through Selective Laser Melting are strongly influenced by their thermal history, which includes the variation of heating and cooling rates, temperature gradients, temperature rises, and many more. The post-production treatment methods are very important for refining the microstructure, and the standard processes include annealing and thermomechanical processing.
  \item The thermal history, mainly the solidification rate, cooling rates, and thermal gradient, also dictates the mechanical properties of the LPBFed part. Most of the mechanical properties are attributed to the refinement of microstructures of grains and hence depend on the thermal history.
  \item As complete melting is a significant LPBF feature, the process is very much prone to melt pool instability. This could also result in microstructural defects if the choice of the process parameters is poor. All the defects have detrimental effects on the properties of the part. Also, larger powder particles are difficult to melt. Therefore, the poor finish of surfaces is observed when LPBF is carried out with coarser and large powders.
  \item One crucial factor dictating defects is the "Staircase" effect related to the increased number of layers: defects, particularly surface roughness, increase with the increase in layer thickness. Hence, the idea is to balance the trade-off between the surface roughness and the product's built-up time.
  \item Artificial intelligence-based machine learning and deep learning techniques were used for process monitoring, predicting product quality, and optimizing process control.
\end{itemize}

\section*{Declaration of competing interest}
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Chander Prakash reports financial support was provided by Lovely Professional University. Chander Prakash reports a relationship with Lovely Professional University that includes: employment. NA.

\section*{Acknowledgments}
The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program 'Priority 2030' (Agreement 075-15-2021-1333 dated 30.09.2021). The authors would like to thank all his masters/PhD students (Mr. Justin Hijam and Mr. Benjamin Das) for their support during the preparation of this manuscript. Also, he would like to acknowledge the support provided by the North Eastern Regional Institute of Science and Technology.

\section*{R E F ER ENCES}
[1] DebRoy T, Wei HL, Zuback JS, Mukherjee T, Elmer JW, Milewski JO, et al. Additive manufacturing of metallic components - process, structure and properties. Prog Mater

Sci 2018;92:112-224. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/}

j.pmatsci.2017.10.001.

[2] Tofail SAM, Koumoulos EP, Bandyopadhyay A, Bose S, O'Donoghue L, Charitidis C. Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Mater Today 2017:1-16. \href{https://doi.org/}{https://doi.org/} 10.1016/j.mattod.2017.07.001.

[3] Nguyen HD, Pramanik A, Basak AK, Dong Y, Prakash C, Debnath $\mathrm{S}$, et al. A critical review on additive manufacturing of Ti-6Al-4V alloy: microstructure and mechanical properties. J Mater Res Technol 2022;18(2022):4641-61. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.jmrt.2022.04.055.

[4] Bandyopadhyay A, Gualtieri T, Bose S. Global engineering and additive manufacturing. 2015. \href{https://doi.org/10.1201/}{https://doi.org/10.1201/} b18893-2.

[5] Attaran M. The rise of 3-D printing: the advantages of additive manufacturing over traditional manufacturing. Bus Horiz 2017;60:677-88. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.bushor.2017.05.011.

[6] Frontiers of engineering 2011. Washington, D.C.: National Academies Press; 2012. \href{https://doi.org/10.17226/13274}{https://doi.org/10.17226/13274}.

[7] Li C, Fu CH, Guo YB, Fang FZ. A multiscale modeling approach for fast prediction of part distortion in selective laser melting. J Mater Process Technol 2016;229:703-12. \href{https://doi.org/10.1016/j.jmatprotec.2015.10.022}{https://doi.org/10.1016/j.jmatprotec.2015.10.022}.

[8] Krueger H. Standardization for additive manufacturing in aerospace. Engineering 2017;3:585. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} J.ENG.2017.05.010

[9] Frazier WE. Metal additive manufacturing: a review. J Mater Eng Perform 2014;23:1917-28. \href{https://doi.org/10.1007/}{https://doi.org/10.1007/} s11665-014-0958-z.

[10] Herzog D, Seyda V, Wycisk E, Emmelmann C. Additive manufacturing of metals. Acta Mater 2016;117:371-92.

[11] Guo N, Leu MC. Additive manufacturing: technology, applications and research needs. Front Mech Eng 2013;8(3):215-43.

[12] Gibson I, Rosen D, Stucker B. Additive manufacturing technologies: 3D printing, rapid prototyping, and direct digital manufacturing. 2nd ed. Berlin: Springer; 2015.

[13] Kannan B, Rajendran DK. A review on status of research in metal additive manufacturing. In: Advances in 3D printing \& additive manufacturing technologies. Singapore: Springer; 2017. p. 95-100.

[14] Shapiro AA, Borgonia JP, Chen QN, Dillon RP, McEnerney B, Polit-Casillas R, et al. Additive manufacturing for aerospace flight applications. J Spacecraft Rockets 2016;53(5):952-9.

[15] Seifi M, Salem A, Beuth J, Harrysson O, Lewandowski JJ. Overview of materials qualification needs for metal additive manufacturing. JOM 2016;68(3):747-64.

[16] O'Leary R, Setchi R, Prickett P, Hankins G, Jones N. An investigation into the recycling of Ti-6Al-4V powder used within SLM to improve sustainability, SDM'2015. Seville, Spain: 2nd International Conference on Sustainable Design and Manufacturing; 2015.

[17] Galarraga H, Warren RJ, Lados DA, Dehoff RR, Kirka MM, Nandwana P. Effects of heat treatments on microstructure and properties of Ti-6Al-4V ELI alloy fabricated by electron beam melting (EBM). Mater Sci Eng, A 2017;685:417-28.

[18] Yan R, Luo D, Huang H, Li R, Yu N, Liu C, et al. Electron beam melting in the fabrication of three-dimensional mesh titanium mandibular prosthesis scaffold. Sci Rep 2018;8(1):750.

[19] \href{https://www.3dhubs.com/knowledge-base/introductionmetal-3d-printing/}{https://www.3dhubs.com/knowledge-base/introductionmetal-3d-printing/}.

[20] \href{https://3dprint.com/19477/ebm-printing-3d-ornl/}{https://3dprint.com/19477/ebm-printing-3d-ornl/}.

[21] \href{http://tusharmahale.wixsite.com/home/obtainium}{http://tusharmahale.wixsite.com/home/obtainium}..\\
[22] \href{https://www.rtejournal.de/ausgabe2/233/view?set_}{https://www.rtejournal.de/ausgabe2/233/view?set\_} language $=\mathrm{en}$.

[23] Wauthle R, Vrancken B, Beynaerts B, Jorissen K, Schrooten J, Kruth J-P, et al. Effects of build orientation and heat treatment on the microstructure and mechanical properties of selective laser melted Ti6Al4V lattice structures. Addit Manuf 2015;5:77-84.

[24] Kok Y, Tan XP, Wang P, Nai MLS, Loh NH, Liu E, et al. Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: a critical review. Mater Des 2017. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.matdes.2017.11.021.

[25] Günther J, Brenne F, Droste M, Wendler M, Volkova O, Biermann $\mathrm{H}$, et al. Design of novel materials for additive manufacturing - isotropic microstructure and high defect tolerance. Sci Rep 2018:8. \href{https://doi.org/10.1038/s41598018-19376-0}{https://doi.org/10.1038/s41598018-19376-0}.

[26] Jo Milewski. Additive manufacturing of metals. Springer series in materials science, 258. Springer; 2017.

[27] Frazier WE. Metal additive manufacturing: a review. J Mater Eng Perform 2014;23(6):1917-28.

[28] Shin YC, Bailey N, Katinas C, Tan W. Predictive modeling capabilities from incident powder and laser to mechanical properties for laser directed energy deposition. Comput Mech 2018;61(5):617-36.

[29] Ahsan F, Ladani L. Temperature profile, bead geometry, and elemental evaporation in laser powder bed fusion additive manufacturing process. JOM 2020;72:429-39. https:// \href{http://doi.org/10.1007/s11837-019-03872-3}{doi.org/10.1007/s11837-019-03872-3}.

[30] Liu S, Shin YC. Simulation and experimental studies on microstructure evolution of resolidified dendritic TiCx in laser direct deposited Ti-TiC composite. Mater Des 2018;159:212-23.

[31] Parry L, Ashcroft IA, Wildman RD. Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation. Addit Manuf 2016;12:1-15.

[32] Tammas-Williams S, Zhao H, Léonard F, Derguti F, Todd I, Prangnell PB. XCT analysis of the influence of melt strategies on defect population in Ti-6Al-4V components manufactured by Selective Electron Beam Melting. Mater Char 2015;102:47-61.

[33] Strantza M, Vafadari R, de Baere D, Vrancken B, van Paepegem W, Vandendael I, et al. Fatigue of Ti6Al4V structural health monitoring systems produced by selective laser melting. Materials 2016;9(2).

[34] Biswas N, Ding JL, Balla VK, Field DP, Bandyopadhyay A. Deformation and fracture behavior of laser processed dense and porous Ti6Al4V alloy under static and dynamic loading. Mater Sci Eng, A 2012;549:213-21.

[35] Beretta S, Romano S. A comparison of fatigue strength sensitivity to defects for materials manufactured by AM or traditional processes. Int J Fatig 2017;94:178-91.

[36] Attar H, Ehtemam-Haghighi S, Kent D, Wu X, Dargusch MS. Comparative study of commercially pure titanium produced by laser engineered net shaping, selective laser melting and casting processes. Mater Sci Eng 2017;705:385. 93.

[37] Gokuldoss PK, Kolla S, Eckert J. Additive manufacturing processes: selective laser melting, electron beam melting and binder jetting-selection guidelines. Materials 2017;10(6):672.

[38] Miyanaji H, Zhang S, Yang L. A new physics-based model for equilibrium saturation determination in binder jetting additive manufacturing process. Int J Mach Tool Manufact 2018;124:1-11.

[39] Thompson MK, Moroni G, Vaneker T, Fadel G, Campbell RI, Gibson I, et al. Design for additive manufacturing: trends,\\
opportunities, considerations, and constraints. CIRP Ann Manuf Technol 2016;65:737-60.

[40] Geetha M, Singh AK, Asokamani R, Gogia AK. Ti based biomaterials, the ultimate choice for orthopaedic implants a review. Prog Mater Sci 2009;54:397-425.

[41] Song X, Wang L, Niinomi M, Nakai M, Liu Y. Fatigue characteristics of a biomedical $\beta$-type titanium alloy with titanium boride. Mater Sci Eng 2015;640:154-64.

[42] Wang K. The use of titanium for medical applications in the USA, vol. 213. Materials Science and Engineering; 1996.

[43] Gu D, Hagedorn YC, Meiners W, Meng G, Batista RJS, Wissenbach K, et al. Densification behavior, microstructure evolution, and wear performance of selective laser melting processed commercially pure titanium. Acta Mater 2012;60:3849-60.

[44] Wysocki B, Maj P, Krawczyńska A, Rożniatowski K, Zdunek J, Kurzydłowski KJ, et al. Microstructure and mechanical properties investigation of $\mathrm{CP}$ titanium processed by selective laser melting (SLM). J Mater Process Technol 2017;241:13-23.

[45] Xu W, Brandt M, Sun S, Elambasseril J, Liu Q, Latham K, et al. Additive manufacturing of strong and ductile Ti-6Al$4 \mathrm{~V}$ by selective laser melting via in situ martensite decomposition. Acta Mater 2015;85:74-84.

[46] Vilaro T, Colin C, Bartout JD. As-fabricated and heat-treated microstructures of the Ti-6Al-4V alloy processed by selective laser melting. Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 2011;42:3190-9.

[47] Thijs L, Verhaeghe F, Craeghs T, Humbeeck J Van, Kruth JP. A study of the microstructural evolution during selective laser melting of Ti-6Al-4V. Acta Mater 2010;58:3303-12.

[48] Vrancken B, Thijs L, Kruth J-P, Van Humbeeck J. Heat treatment of Ti6Al4V produced by selective laser melting: microstructure and mechanical properties. J Alloys Compd 2012;541:177-85.

[49] Leuders S, Thöne M, Riemer A, Niendorf T, Tröster T, Richard HA, et al. On the mechanical behaviour of titanium alloy TiAl6V4 manufactured by selective laser melting: fatigue resistance and crack growth performance. Int J Fatig 2013;48:300. 7.

[50] Molotnikov A, Kingsbury A, Brandt M. Current state and future trends in laser powder bed fusion technology. In: InFundamentals of laser powder bed fusion of metals. Elsevier; 2021 Jan 1. p. 621-34.

[51] Khorasani A, Gibson I, Veetil JK, Ghasemi AH. A review of technological improvements in laser-based powder bed fusion of metal printers. Int J Adv Manuf Technol 2020 May;108(1):191-209.

[52] Haupt B, Neef A, Schwarze D, KrolX TA. Powder application arrangement comprising a camera. Google Patents 2020.

[53] Hu Z. System and method for multi-laser additive manufacturing. Google Patents 2016.

[54] Manvatkar V, De A, DebRoy T. Heat transfer and material flow during laser assisted multi-layer additive manufacturing. J Appl Phys 2014;116(12):124905.

[55] Additive Industries. Industrial solutions for metal additive manufacturing last modified March 20, 2019. Accessed March 21, 2019, \href{https://additiveindustries.com/;}{https://additiveindustries.com/;} 2019.

[56] Scime L, Beuth J. Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process. Addit Manuf 2019;29:100830.

[57] Gradl PR, Protz CS, Cooper K, Ellis D, Evans LJ, Garcia C. GRCop-42 development and hot-fire testing using additive manufacturing powder bed fusion for channel cooled combustion chambers. In: AIAA propulsion and energy 2019 forum. American Institute of Aeronautics and Astronautics; 2019.\\
[58] Elmer JW, Gibbs G. The effect of atmosphere on the composition of wire arc additive manufactured metal components. Sci Technol Weld Join 2019;24(5):367-74.

[59] Wei H, Bhadeshia HKDH, David SA, DebRoy T. Harnessing the scientific synergy of welding and additive manufacturing. Sci Technol Weld Join 2019:1-6.

[60] Fotovvati B, Asadi E. Size effects on geometrical accuracy for additive manufacturing of Ti-6Al-4V ELI parts. Int J Adv Manuf Technol 2019;1-9.

[61] Komzisk L. Computer-aided simulation of additive manufacturing processes. Google Patents 2019.

[62] Trumpf. Additive production systems. Last modified March 20,2019. accessed March 21,2019, \href{https://www.trumpf.com/}{https://www.trumpf.com/} en\_SE/products/machines-systems/additiveproductionsystems/; 2019.

[63] Gasman L. Additive aerospace considered as a business. In: Additive manufacturing for the aerospace industry. Elsevier; 2019. p. 327-40.

[64] Gibson I, Khorasani AM. Metallic additive manufacturing: design, process, and post-processing. Multidisciplinary Digital Publishing Institute; 2019.

[65] SLM Solutions. SLM Machines. 2019. Last modified March 20,2019, \href{https://slm-solutions.com/products/machines}{https://slm-solutions.com/products/machines}. [Accessed 21 March 2019].

[66] Akbari M, Kovacevic R. Closed loop control of melt pool width in robotized laser powder-directed energy deposition process. Int J Adv Manuf Technol 2019;1-12.

[67] Shrestha R, Shamsaei N, Seifi M, Phan N. An investigation into specimen property to part performance relationships for laser beam powder bed fusion additive manufacturing Addit Manuf 2019;29:100807.

[68] Urhal P, Weightman A, Diver C, Bartolo P. Robot assisted additive manufacturing: a review. Robot Comput Integrated Manuf 2019;59:335-45.

[69] SISMA. LMF "laser metal fusion" technology, last modified March 20,2019. 2019. accessed March 21,2019, \href{https://www}{https://www}. \href{http://sisma.com/en/additive-manufacturing/}{sisma.com/en/additive-manufacturing/}.

[70] Jafari R, Cloutier C, Allahdini A, Momen G. Recent progress and challenges with 3D printing of patterned hydrophobic and superhydrophobic surfaces. Int J Adv Manuf Technol 2019:1-14.

[71] Rausch AM, Markl M, Körner C. Predictive simulation of process windows for powder bed fusion additive manufacturing: influence of the powder size distribution. Comput Math Appl 2019;78(7):2351-9.

[72] Ljungblad U. Powder distribution in additive manufacturing. Google Patents 2015.

[73] Rengers SH, Stevenson CX, Welsh CM. Powder recirculating additive manufacturing apparatus and method. Google Patents 2017.

[74] Renishaw. Additive manufacturing products, last modified March 20,2019. \href{https://www.renishaw.com/en/}{https://www.renishaw.com/en/} additivemanufacturing-products\%2D\%2D17475; 2019. Accessed March 21,2019.

[75] Yeung H, Lane B, Fox J. Part geometry and conductionbased laser power control for powder bed fusion additive manufacturing. Addit Manuf 2019;30:100844.

[76] 3D Printing Industry, A.L.I.R.M.D.P.S.B.L.M.S.. acessd October 14, 2019, \href{https://3dprintingindustry.com/news/}{https://3dprintingindustry.com/news/} aurora-labs-increases-rmp1-metal-3d-printing-speed-by2000-162272/; 2019.

[77] Yoo D-J. Recent trends and challenges in computer-aided design of additive manufacturing-based biomimetic scaffolds and bioartificial organs. Int J Precis Eng Manuf 2014;15(10):2205-17.

[78] Roach DJ, Hamel CM, Dunn CK, Johnson MV, Kuang X, Qi HJ. The $\mathrm{m} 4$ 3D printer: a multi-material multi-method additive\\
manufacturing platform for future 3D printed structures. Addit Manuf 2019;29:100819.

[79] Jones LC, ORTNER Jonathan, MOORES John, Kenney Patrick. Modified frame and recoating system. US20190099807A1: Google Patents; 2019.

[80] Jelis E, Hespos M, Groeschler SL, Carpenter R. L-PBF of 4340 low alloy steel: influence of feedstock powder, layer thickness, and machine maintenance. J Mater Eng Perform 2019;28(2):693-700.

[81] Keremes JJ, Haynes JD, Gao Y, Matejczyk DE. Laser configuration for additive manufacturing. Google Patents 2013.

[82] EOS. Systems and equipment for metal manufacturing, last modified March 20,2019. \href{https://www.eos.info/systems_}{https://www.eos.info/systems\_} solutions/metal/systems\_equipment; 2019. Accessed March 21,2019.

[83] Nguyen DS, Park HS, LeeCM. Effect of cleaning gas stream on products in selective laser melting. Mater Manuf Process 2019;34(4):455-61.

[84] Solutions GF Machining. Additive manufacturing, last modified March 20,2019. \href{https://www.gfms.com/com/en/}{https://www.gfms.com/com/en/} Products/advanced-manufacturing/additive-

manufacturing-factory.html; 2019. Accessed March 21,2019.

[85] NXG XII 600, a new SLM® machine \href{https://www.slmpushing-the-limits.com}{https://www.slmpushing-the-limits.com}.

[86] Soylemez E. High deposition rate approach of selective laser melting through defocused single bead experiments and thermal finite element analysis for Ti-6Al-4V. Addit Manuf 2020;31:100984.

[87] Shi R, Khairallah SA, Roehling TT, Heo TW, McKeown JT, Matthews MJ. Microstructural control in metal laser powder bed fusion additive manufacturing using laser beam shaping strategy. Acta Mater 2020 Feb 1;184:284-305.

[88] Iveković A, Montero-Sistiaga ML, Vleugels J, Kruth JP, Vanmeensel K. Crack mitigation in Laser Powder Bed Fusion processed Hastelloy X using a combined numericalexperimental approach. J Alloys Compd 2021 May 25;864:158803.

[89] Okunkova A, Volosova M, Peretyagin P, Vladimirov Y, Zhirnov I, Gusarov AV. Experimental approbation of selective laser melting of powders by the use of nonGaussian power density distributions. Phys Procedia 2014;56:48-57.

[90] Okunkova AA, Peretyagin P, Podrabinnik PA, Zhirnov IV, Gusarov AV. Development of laser beam modulation assets for the process productivity improvement of selective laser melting. Procedia IUTAM 2017;23:177-86.

[91] Wischeropp TM, Tarhini H, Emmelmann C. Influence of laser beam profile on the selective laser melting process of AlSi10Mg. J Laser Appl 2020;32:22059.

[92] Zhang LC, Attar H. Selective laser melting of titanium alloys and titanium matrix composites for biomedical applications: a review. Adv Eng Mater 2016;18:463-75.

[93] Kruth J-P, Mercelis P, van Vaerenbergh J, Froyen L, Rombouts M. Binding mechanisms in selective laser sintering and selective laser melting. Rapid Prototyp J 2005;11:26-36.

[94] Riza S, Masood S, Wen C. Laser-assisted additive manufacturing for metallic biomedical scaffolds. In: Comprehensive materials processing. London, UK: Elsevier; 2014.

[95] Yadroitsev I, Smurov I. Surface morphology in selective laser melting of metal powders. Phys Procedia 2011;12:264-70.

[96] Yadroitsev I, Yadroitsava I, Smurov I. Strategy of fabrication of complex shape parts based on the stability of single laser melted track. SPIE Conference Proceedings 2011:7921.\\
[97] Li Y, Gu D. Parametric analysis of thermal behavior during selective laser melting additive manufacturing of aluminum alloy powder. Mater Des 2014;63:856-67.

[98] Gu D. Laser additive manufacturing of high-performance materials. New York, NY, USA: Springer; 2015.

[99] Zhang H, Zhang D, Ma C, Guo S. Improving mechanical properties and corrosion resistance of $\mathrm{Mg}-6 \mathrm{Zn}-\mathrm{Mn}$ magnesium alloy by rapid solidification. Mater Lett 2013;92:45-8.

[100] Kruth J-P, Badrossamay M, Yasa E, Deckers J, Thijs L, van Humbeeck J. Part and material properties in selective laser melting of metals. In: Proceedings of the 16th international symposium on electromachining, Shanghai, China; April 2010. p. 19-23.

[101] Kruth J-P, Levy G, Klocke F, Childs T. Consolidation phenomena in laser and powder-bed based layered manufacturing. CIRP Journal of Manufacturing Science and Technology 2007;56:730-59.

[102] Yap C, Chua C, Dong Z, Liu Z, Zhang D, Loh L, et al. Review of selective laser melting: materials and applications. Appl Phys Rev 2015;2:041101.

[103] Gurrappa I. Characterization of titanium alloy Ti6Al4V for chemical, marine and industrial applications. Mater Char 2003;51:131-9. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.matchar.2003.10.006.

[104] Wang SQ, Liu JH, Chen DL. Effect of strain rate and temperature on strain hardening behavior of a dissimilar joint between Ti6Al4V and Ti17 alloys. Mater Des 2014;56:174-84. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.matdes.2013.11.003.

[105] Banerjee D, Williams JC. Perspectives on titanium science and technology. Acta Mater 2013;61:844-79.

[106] Attar H. 'Manufacturing and properties of titanium-based materials produced by selective laser melting'. PhD thesis. Edith Cowan University; 2015.

[107] Gepreel MAH, Niinomi M. Biocompatibility of Ti-alloys for long-term implantation. J Mech Behav Biomed Mater 2013;20:407-15.

[108] Attar H, Calin M, Zhang LC, Scudino S, Eckert J. Manufacture by selective laser melting and mechanical behavior of commercially pure titanium. Mater Sci Eng 2014;593:170-7. \href{https://doi.org/10.1016/j.msea.2013.11.038}{https://doi.org/10.1016/j.msea.2013.11.038}.

[109] Vrancken B, Thijs L, Kruth JP, Van Humbeeck J. Microstructure and mechanical properties of a novel $\beta$ titanium metallic composite by selective laser melting. Acta Mater 2014;68:150-8. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.actamat.2014.01.018.

[110] Cain V, Thijs L, Van Humbeeck J, Van Hooreweder B, Knutsen R. Crack propagation and fracture toughness of Ti6Al4V alloy produced by selective laser melting. Addit Manuf 2015;5:68-76. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.addma.2014.12.006.

[111] Yang C, Zhao YJ, Kang LM, Li DD, Zhang WW, Zhang LC. High-strength silicon brass manufactured by selective laser melting. Mater Lett 2018;210:169-72. \href{https://doi.org/}{https://doi.org/} 10.1016/j.matlet.2017.09.011.

[112] Kang N, Coddet P, Liao H, Baur T, Coddet C. Wear behavior and microstructure of hypereutectic Al-Si alloys prepared by selective laser melting. Appl Surf Sci 2016;378:142-9. \href{https://doi.org/10.1016/j.apsusc.2016.03.2}{https://doi.org/10.1016/j.apsusc.2016.03.2}.

[113] Ren YM, Lin X, Fu X, Tan H, Chen J, Huang WD. Microstructure and deformation behavior of Ti-6Al-4V alloy by high-power laser solid forming. Acta Mater 2017;132:82-95. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.actamat.2017.04.026.

[114] Yao J, Suo T, Zhang S, Zhao F, Wang H, Liu J, et al. Influence of heat treatment on the dynamic behavior of 3D laser-\\
deposited Ti-6Al-4V alloy. Mater Sci Eng 2016;677:153-62. \href{https://doi.org/10.1016/j.msea.2016.09.036}{https://doi.org/10.1016/j.msea.2016.09.036}.

[115] Davari N, Rostami A, Abbasi SM. Effects of annealing temperature and quenching medium on microstructure, mechanical properties as well as fatigue behavior of Ti-6Al4V alloy. Mater Sci Eng 2017;683:1-8. \href{https://doi.org/}{https://doi.org/} 10.1016/j.msea.2016.11.095.

[116] Brailovski V, Prokoshkin S, Gauthier M, Inaekyan K, Dubinskiy S, Petrzhik M, et al. Bulk and porous metastable beta Ti-Nb- $\mathrm{Zr}(\mathrm{Ta})$ alloys for biomedical applications. Mater Sci Eng C 2011;31:643-57. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.msec.2010.12.008.

[117] Fukuda A, Takemoto M, Saito T, Fujibayashi S, Neo M, Yamaguchi S, et al. Bone bonding bioactivity of Ti metal and Ti-Zr-Nb-Ta alloys with $\mathrm{Ca}$ ions incorporated on their surfaces by simple chemical and heat treatments. Acta Biomater 2011;7:1379-86. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.actbio.2010.09.026.

[118] Lin CW, Ju CP, Chern Lin JH. A comparison of the fatigue behavior of cast Ti- 7.5Mo with c.p. titanium, Ti-6Al-4V and Ti-13Nb-13Zr alloys. Biomaterials 2005;26:2899-907. https:// \href{http://doi.org/10.1016/j.biomaterials.2004.09.007}{doi.org/10.1016/j.biomaterials.2004.09.007}.

[119] Shukla AK, Balasubramaniam R. Effect of surface treatment on electrochemical behavior of CP Ti, Ti-6Al-4V and Ti$13 \mathrm{Nb}-13 \mathrm{Zr}$ alloys in simulated human body fluid. Corrosion Sci 2006;48:1696-720. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.corsci.2005.06.003.

[120] Cvijović-Alagić I, Cvijović Z, Mitrović S, Panić V, Rakin M. Wear and corrosion behaviour of Ti-13Nb-13Zr and Ti-6Al$4 \mathrm{~V}$ alloys in simulated physiological solution. Corrosion Sci 2011;53:796-808. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.corsci.2010.11.014.

[121] Fischer M, Joguet D, Robin G, Peltier L, Laheurte P. In situ elaboration of a binary Ti-26Nb alloy by selective laser melting of elemental titanium and niobium mixed powders. Mater Sci Eng C 2016;62:852-9. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.msec.2016.02.033.

[122] Zhou L, Yuan T, Li R, Tang J, Wang M, Mei F. Microstructure and mechanical properties of selective laser melted biomaterial Ti-13Nb-13Zr compared to hot forging. Mater Sci Eng 2018;725:329-40. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.msea.2018.04.001.

[123] Zhou L, Yuan T, Li R, Tang J, Wang M, Mei F. Anisotropic mechanical behavior of biomedical Ti-13Nb-13Zr alloy manufactured by selective laser melting. J Alloys Compd 2018;762:289-300. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.jallcom.2018.05.179.

[124] Dimić I, Cvijović-Alagić I, Völker B, Hohenwarter A, Pippan R, Veljović D, et al. Microstructure and metallic ion release of pure titanium and Ti-13Nb-13Zr alloy processed by high pressure torsion. Mater Des 2016;91:340-7. https:// \href{http://doi.org/10.1016/j.matdes.2015.11.088}{doi.org/10.1016/j.matdes.2015.11.088}.

[125] Mohan L, Anandan C. Wear and corrosion behavior of oxygen implanted biomedical titanium alloy Ti-13Nb-13Zr. Appl Surf Sci 2013;282:281-90. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.apsusc.2013.05.120

[126] Khan MA, Williams RL, Williams DF. The corrosion behaviour of Ti-6Al-4V, Ti- $6 \mathrm{Al}-7 \mathrm{Nb}$ and Ti-13Nb-13Zr in protein solutions. Biomaterials 1999;20:631-7. https:// \href{http://doi.org/10.1016/S0142-9612(98)00217-8}{doi.org/10.1016/S0142-9612(98)00217-8}.

[127] Geetha M, Kamachi Mudali U, Gogia AK, Asokamani R, Raj B. Influence of microstructure and alloying elements on corrosion behavior of $\mathrm{Ti}-13 \mathrm{Nb}-13 \mathrm{Zr}$ alloy. Corrosion Sci 2004;46:877-92. \href{https://doi.org/10.1016/S0010-938X(03)}{https://doi.org/10.1016/S0010-938X(03)} 00186-0.

[128] Urbańczyk E, Krzakała A, Kazek-Kesik A, Michalska J, Stolarczyk A, Dercz G, et al. Electrochemical modification of Ti-13Nb-13Zr alloy surface in phosphate based solutions.\\
Surf Coating Technol 2016;291:79-88. \href{https://doi.org/}{https://doi.org/} 10.1016/j.surfcoat.2016.02.025

[129] Bobbili R, Madhu V. Dynamic recrystallization behavior of a biomedical Ti-13Nb-13Zr alloy. J Mech Behav Biomed Mater 2016;59:146-55. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.jmbbm.2015.12.025.

[130] Quan G, Zhang L, Wang X, Li Y. Correspondence between microstructural evolution mechanisms and hot processing parameters for Ti-13Nb-13Zr biomedical alloy in comprehensive processing maps. J Alloys Compd 2017;698:200-6. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.jallcom.2016.12.140.

[131] Zhang L, Song B, Fu JJ, Wei SS, Yang L, Yan CZ, et al. Topology-optimized lattice structures with simultaneously high stiffness and light weight fabricated by selective laser melting: design, manufacturing and characterization. J Manuf Process August 2020;56:1166-77.

[132] Wally Zena J, Haque Abdul M, Feteira Antonio, Claeyssens Frederik, RussellGoodall, Reilly Gwendolen C. Selective laser melting processed Ti6Al4V lattices with graded porosities for dental applications. J Mech Behav Biomed Mater 2019;90:20-9.

[133] Maskery I, Aremu AO, Parry L, Wildman RD, Tuck CJ, Ashcroft IA. Effective design and simulation of surfacebased lattice structures featuring volume fraction and cell type grading. Mater Des 2018;155:220-32.

[134] Zhang Lei, Song Bo, Yang Lei, Shi Yusheng. Tailored mechanical response and mass transport characteristic of selective laser melted porous metallic biomaterials for bone scaffolds. Acta Biomater 2020;112:298-315.

[135] Xu Yangli, Zhang Dongyun, Hu Songtao, Chen Runping, Gu Yilei, Kong Xiangsen, et al. Mechanical properties tailoring of topology optimized and selective laser melting fabricated Ti6Al4V lattice structure. J Mech Behav Biomed Mater 2019;99:225-39.

[136] Zhang Xiang-Yu, Fang Gang, Sander Leeflang, Zadpoor Amir A, Zhou Jie. Topological design, permeability and mechanical behavior of additively manufactured functionally graded porous metallic biomaterials. Acta Biomater 2019;84:437-52.

[137] Chen LY, Huang JC, Lin CH, Pan CT, Chen SY, Yang TL, et al. Anisotropic response of Ti-6Al-4V alloy fabricated by 3D printing selective laser melting. Mater Sci Eng, A 2017;682:389-95.

[138] Qiu C, Adkins NJE, Attallah MM. Microstructure and tensile properties of selectively laser-melted and of HIPed lasermelted Ti-6Al-4V Materials Science and Engineering. A 2013;578:230-9.

[139] Shi X, Ma S, Liu C, Wu Q, Lu J, Liu Y, et al. Selective laser melting-wire arc additive manufacturing hybrid fabrication of Ti-6Al-4V alloy: microstructure and mechanical properties Materials Science and Engineering. A 2017;684:196-204.

[140] Zhang LC, Klemm D, Eckert J, Hao YL, Sercombe TB. Manufacture by selective laser melting and mechanical behavior of a biomedical Ti-24Nb-4Zr-8Sn alloy. Scripta Mater 2011;65:21-4.

[141] Ahmed T, Rack HJ. Phase transformations during cooling in a + b titanium alloys. Mater Sci Eng, A 1998;243:206-11.

[142] Güc,lü FM, C, imenoğlu H. The recrystallization behaviour of CP-titanium. Mater Sci Forum 2004;467:459-64.

[143] Simonelli M, Tse YY, Tuck C. Effect of the build orientation on the mechanical properties and fracture modes of SLM Ti6Al-4V. Mater Sci Eng, A 2014;616:1-11.

[144] Xu W, Lui EW, Pateras A, Qian M, Brandt M. In situ tailoring microstructure in additively manufactured Ti-6Al-4V for superior mechanical performance. Acta Mater 2017;125:390-400.

[145] Al-Bermani SS, Blackmore ML, Zhang W, Todd I. The origin of microstructural diversity, texture, and mechanical properties in electron beam melted Ti-6Al-4V. Metall Mater Trans 2010;41:3422-34.

[146] Lin CW, Ju CP, Lin JHC. Comparison among mechanical properties of investment-cast $\mathrm{CP}-\mathrm{Ti}, \mathrm{Ti}-6 \mathrm{Al}-7 \mathrm{Nb}$ and Ti-15Mo-1Bi alloys. Mater Trans 2004;45:3028-32.

[147] Zhang L-C, Attar H, Calin M, Eckert J. Review on manufacture by selective laser melting and properties of titanium based materials for biomedical applications. Materials Technology; 2016. \href{https://doi.org/10.1179/}{https://doi.org/10.1179/} $1753555715 Y .0000000076$.

[148] Lütjering G. Influence of processing on microstructure and mechanical properties of $(\alpha+\beta)$ titanium alloys. Mater Sci Eng, A 1998;243:32-45.

[149] Xu W, Sun S, Elambasseril J, Liu Q, Brandt M, Qian M. Ti-6Al4V additively manufactured by selective laser melting with superior mechanical properties. J Met 2015;67:668-73.

[150] Simonelli M, Tse YY, Tuck C. The formation of $(\alpha+\beta)$ microstructure in as-fabricated selective laser melting of Ti6Al-4V. J Mater Res 2014;29:2028-35.

[151] Thijs L, Verhaeghe F, Craeghs T, Van Humbeeck J, Kruth J-P. A study of the microstructural evolution during selective laser melting of Ti-6Al-4V. Acta Mater 2010;58:3303-12.

[152] Murr LE, Quinones SA, Gaytan SM, Lopez MI, Rodela A, Martinez EY, et al. Microstructure and mechanical behavior of $\mathrm{Ti}-6 \mathrm{Al}-4 \mathrm{~V}$ produced by rapid-layer manufacturing, for biomedical applications. J Mech Behav Biomed Mater 2009;2:20-32.

[153] Sercombe T, Jones N, Day R, Kop A. Heat treatment of $\mathrm{Ti}-6 \mathrm{Al}-7 \mathrm{Nb}$ components produced by selective laser melting. Rapid Prototyp J 2008;14:300-4.

[154] Lo' pez M, Gutiérrez A, Jiménez JA. In vitro corrosion behaviour of titanium alloys without vanadium. Electrochim Acta 2002;47:1359-64.

[155] Liu X, Chu PK, Ding C. Surface modification of titanium, titanium alloys, and related materials for biomedical applications. Mater Sci Eng R 2004;47:49-121.

[156] Chlebus E, Kuźnicka B, Kurzynowski T, Dybała B. Microstructure and mechanical behaviour of Ti-6Al-7Nb alloy produced by selective laser melting. Mater Char 2011;62:488-95.

[157] Aboulkhair NT, Simonelli M, Parry L, Ashcroft I, Tuck C. 3D printing of Aluminium alloys: additive manufacturing of aluminium alloys using selective laser melting. Prog Mater Sci 2019;106.

[158] Attar H, Bönisch M, Calin M, Zhang LC, Scudino S, Eckert J. Selective laser melting of in situ titanium-titanium boride composites: processing, microstructure and mechanical properties. Acta Mater 2014;76:13-22.

[159] Gu DD, Hagedorn Y-C, Meiners W, Wissenbach K, Poprawe R. Nanocrystalline TiC reinforced Ti matrix bulkform nanocomposites by selective laser melting (SLM): densification, growth mechanism and wear behavior. Compos Sci Technol 2011;71:1612-20.

[160] Ma ZY, Tjong SC, Gen L. In-situ Ti-TiB metal-matrix composite prepared by a reactive pressing process. Scripta Mater 2000;42:367-73.

[161] Morsi K, Patel VV. Processing and properties of titaniumtitanium boride (TiBw) matrix composites-a review. J Mater Sci 2007;42:2037-47.

[162] Attar H, Bö nisch M, Calin M, Zhang LC, Zhuravleva K, Funk A, et al. Comparative study of microstructures and mechanical properties of in situ Ti-TiB composites produced by selective laser melting, powder metallurgy, and casting technologies. J Mater Res Technol 2014;29:1941-50.\\
[163] Pattanayak DK, Fukuda A, Matsushita T, Takemoto M, Fujibayashi S, Sasaki K, et al. Bioactive Ti metal analogous to human cancellous bone: fabrication by selective laser melting and chemical treatments. Acta Biomater 2011;7:1398-406.

[164] Attar H, Prashanth KG, Chaubey AK, Calin M, Zhang LC, Scudino S, et al. Comparison of wear properties of commercially pure titanium prepared by selective laser melting and casting processes. Mater Lett 2015;142:38-41.

[165] Warnke PH, Douglas T, Wollny P, Sherry E, Steiner M, Galonska S, et al. Rapid prototyping: porous titanium alloy scaffolds produced by selective laser melting for bone tissue engineering. Tissue Engineering C Methods 2008;15:115-24.

[166] Sun J, Yang Y, Wang D. Mechanical properties of a Ti6Al4V porous structure produced by selective laser melting. Mater Des 2013;49:545-52.

[167] Gupta M, Sharon NML. Magnesium, magnesium alloys, and magnesium composites. Hoboken, NJ, USA: John Wiley \& Sons; 2011.

[168] Mordike B, Ebert T. Magnesium: properties, applications and potential. Mater Sci Eng, A 2001;302:37-45.

[169] Somekawa H, Kinoshita A, Kato A. Effect of alloying elements on room temperature stretch formability in $\mathrm{Mg}$ alloys. Mater Sci Eng, A 2018;732:21-8.

[170] Ding Z, Liu W, Sun H, Li S, Zhang D, Zhao Y, et al. Origins and dissociation of pyramidal <c + a>dislocations in magnesium and its alloys. Acta Mater 2018;146:265-72.

[171] Froes F, Eliezer D, Aghion E. The science, technology, and applications of magnesium. J Met 1998;50:30-4.

[172] Winzer N, Atrens A, Song G, Ghali E, Dietzel W, Kainer KU, et al. A critical review of the stress corrosion cracking (SCC) of magnesium alloys. Adv Eng Mater 2005;7:659-93.

[173] Song GL, Atrens A. Corrosion mechanisms of magnesium alloys. Adv Eng Mater 1999;1:11-33.

[174] Jun J, Kim J, Park B, Kim K, Jung W. Effects of rare earth elements on microstructure and high temperature mechanical properties of ZC63 alloy. J Mater Sci 2005;40:2659-61.

[175] Itoi T, Takahashi K, Moriyama H, Hirohashi M. A highstrength Mg-Ni-Y alloy sheet with a long-period ordered phase prepared by hot-rolling. Scripta Mater 2008;59:1155-8.

[176] Toda-Caraballo I, Galindo-Nava EI, Rivera-Díaz-delCastillo PE. Understanding the factors influencing yield strength on Mg alloys. Acta Mater 2014;75:287-96.

[177] Johnston S, Shi Z, Atrens A. The influence of pH on the corrosion rate of high-purity Mg, AZ91 and ZE41 in bicarbonate buffered Hanks' solution. Corrosion Sci 2015;101:182-92.

[178] Agarwal S, Curtin J, Duffy B, Jaiswal S. Biodegradable magnesium alloys for orthopaedic applications: a review on corrosion, biocompatibility and surface modifications. Mater Sci Eng C 2016;68:948-63.

[179] Němec M, Jäger A, Tesař K, Gärtnerová V. Influence of alloying element $\mathrm{Zn}$ on the microstructural, mechanical and corrosion properties of binary Mg-Zn alloys after severe plastic deformation. Mater Char 2017;134:69-75.

[180] Zhao C, Chen X, Pan F, Wang J, Gao S, Tu T, et al. Strain hardening of as-extruded Mg-xZn ( $\mathrm{x}=1,2,3$ and $4 \mathrm{wt} \%$ ) alloys. J Mater Sci Technol 2019;35:142-50.

[181] Jiang J, Ni S, Yan H, Wu Q, Song M. New orientations between beta '(2) phase and alpha matrix in a Mg-Zn-Mn alloy processed by high strain rate rolling. J Alloys Compd 2018;750:465-70.

[182] Ying T, Zheng MY, Li ZT, Qiao XG, Xu SW. Thermal conductivity of as-cast and as-extruded binary $\mathrm{Mg}-\mathrm{Zn}$ alloys. J Alloys Compd 2015;621:250-5.

[183] Yan Y, Cao H, Kang Y, Yu K, Xiao T, Luo J, et al. Effects of Zn concentration and heat treatment on the microstructure, mechanical properties and corrosion behavior of asextruded Mg-Zn alloys produced by powder metallurgy. J Alloys Compd 2017;693:1277-89.

[184] Farzadfar SA, Martin É, Sanjari M, Essadiqi E, Wells MA, Yue S. On the deformation, recrystallization and texture of hot-rolled Mg-2.9Y and Mg-2.9Zn solid solution alloys-a comparative study. Mater Sci Eng, A 2012;534:209-19.

[185] Němec M, Gärtnerová V, Jäger A. Influence of severe plastic deformation on intermetallic particles in Mg-12 wt.\%Zn alloy investigated using transmission electron microscopy. Mater Char 2016;119:129-36.

[186] Yuasa M, Miyazawa N, Hayashi M, Mabuchi M, Chino Y. Effects of group II elements on the cold stretch formability of Mg-Zn alloys. Acta Mater 2015;83:294-303.

[187] Ng C, Savalani M, Man H, Gibson I. Layer manufacturing of magnesium and its alloy structures for future applications. Virtual Phys Prototyp 2010;5:13-9.

[188] Ng CC, Savalani MM, Man HC. Fabrication of magnesium using selective laser melting technique. Rapid Prototyp J 2011;17:479-90.

[189] Ng CC, Savalani MM, Lau ML, Man HC. Microstructure and mechanical properties of selective laser melted magnesium. Appl Surf Sci 2011;257:7447-54.

[190] Savalani MM, Pizarro JM, Campbell RI, Gibson I. Effect of preheat and layer thickness on selective laser melting (SLM) of magnesium. Journal of Rapid Prototyping 2016;22:115-22.

[191] Zhang B, Liao H, Coddet C. Effects of processing parameters on properties of selective laser melting $\mathrm{Mg}-9 \% \mathrm{Al}$ powder mixture. Mater Des 2012;34:753-8.

[192] Jauer, L.; Jülich, B.; Voshage, M.; Meiners, W., Selective laser melting of magnesium alloys. In Proceedings of the 7th symposium on biodegradable metals, Carovigno, Italy, vols. 23-28 August 2015; p. 1.

[193] Manakari Vyasaraj, Parande Gururaj, Gupta Manoj. Selective laser melting of magnesium and magnesium alloy powders: a review. Basel, Switzerland: MDPI; 2016.

[194] Das S. Physical aspects of process control in selective laser sintering of metals. Adv Eng Mater 2003;5:701-11.

[195] Gieseke M, Noelke C, Kaierle S, Wesling V, Haferkamp H, Hort N. Selective laser melting of magnesium and magnesium alloys. Magnesium Technology 2013;2013:65-8.

[196] Song B, Dong S, Deng S, Liao H, Coddet C. Microstructure and tensile properties of iron parts fabricated by selective laser melting. Opt Laser Technol 2014;56:451-60.

[197] Hu D, Wang Y, Zhang D, Hao L, Jiang J, Li Z, et al. Experimental investigation on selective laser melting of bulk net-shape pure magnesium. Mater Manuf Process 2015;30:1298-304.

[198] Williams JC, Jr EAS. Progress in structural materials for aerospace systems. Acta Mater 2003;51:5775-99.

[199] Heinz A, Haszler A, Keidel C, Moldenhauer S, Benedictus R, Miller WS. Recent development in aluminium alloys for aerospace applications. Mater Sci Eng, A 2000;280:102-7.

[200] Xu W, Luo Y, Zhang Wei, Fu M. Comparative study on local and global mechanical properties of bobbin tool and conventional friction stir welded 7085-T7452 aluminum thick plate. J Mater Sci Technol 2018;34:173-84.

[201] Cui C, Shen Y, Meng F. Review on fabrication methods of in situ metal matrix composites. J Mater Sci Technol 2000;16:619-26.

[202] Ranganath S. J Mater Sci Technol 1997;32:1-16.

[203] Tjong SC, Ma ZY. Microstructural and mechanical characteristics of in situ metal matrix composites. Mater Sci Eng R 2000;29:49-113.

[204] He L, Kang J, Huang T, Rong K. Heat Treat Met 2004;31:69-72.\\
[205] Li W, Yang K, Yin S, Yang X, Xu Y, Lupoi R. Solid-state additive manufacturing and repairing by cold spraying: a review. J Mater Sci Technol 2018;34:440-57.

[206] Mishra RS, Ma ZY. Friction stir welding and processing. Mater Sci Eng R 2005;50:1-78.

[207] Nie F, Dong H, Chen S, Li P, Wang L, Zhao Z, et al. Microstructure and mechanical properties of pulse MIG welded 6061/A356 aluminum alloy dissimilar butt joints. J Mater Sci Technol 2018;34:551-60.

[208] Brandl E, Heckenberger U, Holzinger V, Buchbinder D. Additive manufactured AlSi10Mg samples using selective laser melting (SLM): microstructure, high cycle fatigue, and fracture behavior. Mater Des 2012;34:159-69.

[209] Louvis E, Fox P, Sutcliffe CJ. Selective laser melting of aluminium components. J Mater Process Technol 2011;211:275-84.

[210] Olakanmi EO. Selective laser sintering/melting (SLS/SLM) of pure $\mathrm{Al}, \mathrm{Al}-\mathrm{Mg}$, and $\mathrm{Al}-\mathrm{Si}$ powders: effect of processing conditions and powder properties. J Mater Process Technol 2013;213:1387-405.

[211] Olakanmi EO. Direct selective laser sintering of aluminium alloy powders. Leeds: University of Leeds; 2008. PhD thesis.

[212] Khan M, Dickens P. Selective laser melting (SLM) of gold (Au). Rapid Prototyp J 2012;18:81-94.

[213] Saedi S, Shayesteh Moghaddam N, Amerinatanzi A, Elahinia M, Karaca HE. On the effects of selective laser melting process parameters on microstructure and thermomechanical response of Ni-rich NiTi. Acta Mater 2018;144:552-60.

[214] Simonelli M, Tuck C, Aboulkhair NT, Maskery I, Ashcroft I, Wildman RD, et al. A study on the laser spatter and the oxidation reactions during selective laser melting of 316L stainless steel, Al-Si10-Mg, and Ti-6Al-4V. Metall Mater Trans 2015;46:3842-51.

[215] Yap CY, Chua CK, Dong ZL. An effective analytical model of selective laser melting. Virtual Phys Prototyp 2016;11:21-6.

[216] Yadroitsev I, Gusarov A, Yadroitsava I, Smurov I. Single track formation in selective laser melting of metal powders. J Mater Process Technol 2010;210:1624-31.

[217] Gusarov AV, Yadroitsev I, Bertrand P, Smurov I. Heat transfer modelling and stability analysis of selective laser melting. Appl Surf Sci 2007;254:975-9.

[218] Wei K, Gao M, Wang Z, Zeng X. Effect of energy input on formability, microstructure and mechanical properties of selective laser melted AZ91D magnesium alloy. Mater Sci Eng 2014;611:212-22.

[219] Amorim FL, Lohrengel A, Neubert V, Higa CF, Czelusniak T. Selective laser sintering of Mo-CuNi composite to be used as EDM electrode. Rapid Prototyp J 2014;20:59-68.

[220] Wei K, Wang Z, Zeng X. Influence of element vaporization on formability, composition, microstructure, and mechanical performance of the selective laser melted $\mathrm{Mg}$ Zn-Zr components. Mater Lett 2015;156:187-90.

[221] Li R, Liu J, Shi Y, Wang L, Jiang W. Balling behavior of stainless steel and nickel powder during selective laser melting process. Int J Adv Manuf Technol 2011;59:1025-35.

[222] Qiu YD, Wu JM, Chen AN, Chen P, Yang Y, Liu RZ, et al. Balling phenomenon and cracks in alumina ceramics prepared by direct selective laser melting assisted with pressure treatment. Ceram Int 2020;46(9):13854-61.

[223] Hon KKB. Digital additive manufacturing: from rapid prototyping to rapid manufacturing. London, UK: Springer; 2007. p. 337-40.

[224] Olakanmi EO, Dalgarno KW, Cochrane RF. Laser sintering of blended Al-Si powders. Rapid Prototyp J 2012;18:109-19.

[225] Geng Y, Wang Y, Xu J, Mi S, Fan S, Xiao Y, et al. A highstrength AlSiMg1. 4 alloy fabricated by selective laser melting. J Alloys Compd 2021;867:159103.

[226] Ma M, Wang Z, Gao M, Zeng X. Layer thickness dependence of performance in high-power selective laser melting of 1Cr18Ni9Ti stainless steel. J Mater Process Technol 2015;215:142-50.

[227] Agarwala M, Bourell D, Beaman J, Marcus H, Barlow J. Direct selective laser sintering of metals. Rapid Prototyp J 1995;1(1):26-36.

[228] Olakanmi E, Cochrane R, Dalgarno K. Densification mechanism and microstructural evolution in selective laser sintering of Al-12Si powders. J Mater Process Technol 2011;211:113-21.

[229] Tolochko N, Mozzharov S, Laoui T, Froyen L. Selective laser sintering of single-and two-component metal powders. Rapid Prototyp J 2003;9:68-78.

[230] Suzuki A, Miyasaka T, Takata N, Kobashi M, Kato M. Control of microstructural characteristics and mechanical properties of AlSi12 alloy by processing conditions of laser powder bed fusion. Addit Manuf 2021;48:102383.

[231] Dewidar MM, Dalgarno KW, Wright CS. Processing conditions and mechanical properties of high-speed steel parts fabricated using direct selective laser sintering. Proc Inst Mech Eng, Part B 2003;217:1651-62.

[232] Su WN, Erasenthiran P, Dickens PM. Investigation of fully dense laser sintering of tool steel powder using a pulsed Nd:YAG laser. Proc IMechE Part C: J Mech Eng Sci 2003;217(1):127-38.

[233] Aboulkhair NT, Everitt NM, Ashcroft I, Tuck C. Reducing porosity in AlSi10Mg parts processed by selective laser melting. Addit Manuf 2014;1-4:77-86.

[234] Thijs L, Montero Sistiaga ML, Wauthle R, Xie Q, Kruth J-P, Van Humbeeck J. Strong morphological and crystallographic texture and resulting yield strength anisotropy in selective laser melted tantalum. Acta Mater 2013;61:4657-68.

[235] AlMangour B, Grzesiak D, Yang J-M. Scanning strategies for texture and anisotropy tailoring during selective laser melting of TiC/316L stainless steel nanocomposites. J Alloys Compd 2017;728:424-35.

[236] Thijs L, Kempen K, Kruth J-P, Van Humbeeck J. Finestructured aluminium products with controllable texture by selective laser melting of pre-alloyed AlSi10Mg powder. Acta Mater 2013;61:1809-19.

[237] Li X, Kang C, Huang H, Sercombe T. The role of a lowenergy-density re-scan in fabricating crack-free Al85Ni5Y6Co2Fe2 bulk metallic glass composites via selective laser melting. Mater Des 2014;63:407-11.

[238] Yasa E, Deckers J, Kruth JP. The investigation of the influence of laser re-melting on density, surface quality and microstructure of selective laser melting parts. Rapid Prototyp J 2011;17:312-27.

[239] Sames W, List F, Pannala S, Dehoff R, Babu S. The metallurgy and processing science of metal additive manufacturing. Int Mater Rev 2016;61:315-60.

[240] Attar H, Prashanth KG, Zhang L-C, Calin M, Okulov IV, Scudino S, et al. Effect of powder particle shape on the properties of in situ Ti-TiB composite materials produced by selective laser melting. J Mater Sci Technol 2015;31:1001-5.

[241] Ozbilen A, Unal A, Sheppard T. Influence of oxygen on morphology and oxide content of gas atomiosed aluminum powders. In: Small WM, editor. Physical chemistry of powder metals- production and processing. The minerals, metals and materials society; 1989.

[242] Niu H, Chang I. Selective laser sintering of gas and water atomized high speed steel powders. Scripta Mater 1999;41:25-30.

[243] Simchi A. The role of particle size on the laser sintering of iron powder. Metall Mater Trans B 2004;35:937-48.\\
[244] Glardon R, Karapatis N, Romano V, Levy G. Influence of Nd:YAG parameters on the selective laser sintering of metallic powders. CIRP Ann - Manuf Technol 2001;50:133-6.

[245] Shen N, Chou K. Thermal modeling of electron beam additive manufacturing process: powder sintering effects. In: Proceedings of the ASME 2012 international manufacturing science and engineering conference collocated with the 40th North American manufacturing research conference and in participation with the international conference on tribology materials and processing. USA: Notre Dame; 4-8 June 2012. p. 287-95.

[246] Heiden MJ, Deibler LA, Rodelas JM, Koepke JR, Tung DJ, Saiz DJ, et al. Evolution of 316L stainless steel feedstock due to laser powder bed fusion process. Addit Manuf 2019 Jan 1;25:84-103.

[247] Riener K, Albrecht N, Ziegelmeier S, Ramakrishnan R, Haferkamp L, Spierings AB, et al. Influence of particle size distribution and morphology on the properties of the powder feedstock as well as of AlSi10Mg parts produced by laser powder bed fusion (LPBF). Addit Manuf 2020 Aug 1;34:101286.

[248] Muñiz-Lerma JA, Nommeots-Nomm A, Waters KE, Brochu M. A comprehensive approach to powder feedstock characterization for powder bed fusion additive manufacturing: a case study on AlSi7Mg. Materials 2018;11:2386. \href{https://doi.org/10.3390/ma11122386}{https://doi.org/10.3390/ma11122386}.

[249] Santecchia E, Spigarelli S, Cabibbo M. Material reuse in laser powder bed fusion: side effects of the laser-metal powder interaction. Metals 2020;10:341. \href{https://doi.org/10.3390/}{https://doi.org/10.3390/} met10030341.

[250] Hupfeld T, Sommereyns A, Riahi F, Doñate-Buendía C, Gann S, Schmidt M, et al. Analysis of the nanoparticle dispersion and its effect on the crystalline microstructure in carbon-additivated PA12 feedstock material for laser powder bed fusion. Materials 2020;13:3312. \href{https://doi.org/}{https://doi.org/} 10.3390/ma13153312.

[251] Kusoglu IM, Gökce B, Barcikowski S. Research trends in laser powder bed fusion of $\mathrm{Al}$ alloys within the last decade. Addit Manuf 2020 Dec 1;36:101489.

[252] Kusoglu IM, Doñate-Buendía C, Barcikowski S, Gökce B. Laser powder bed fusion of polymers: quantitative research direction indices. Materials 2021;14:1169. \href{https://doi.org/}{https://doi.org/} 10.3390/ma14051169.

[253] German RM. The importance of particle characteristics in powder injection moulding. Rev Partic Mater 1993;1:109-60.

[254] German RM. Sintering theory and practice. New York: John Wiley \& Sons; 1996.

[255] German RM, Bulger M. A model for densification by sintering of bimodal particle size distribution. Int J Powder Metall 1992;28(3):301-11.

[256] Kumar S. 10.05—selective laser sintering/melting A2-hashmi, saleem. In: Batalha GF, Tyne CJV, Yilbas B, editors. Comprehensive materials processing. Oxford, UK: Elsevier; 2014. p. 93-134.

[257] Liu ZY, Sercombe TB, Schaffer GB. The effect of particle shape on the sintering of aluminium. Metall Mater Trans 2007;38:1351-7.

[258] Niu HJ, Chang ITH. Selective laser sintering of gas and water atomized high speed steel powders. Scripta Mater 1999;41(1):25-30.

[259] Zhang DQ, Liu ZH, Cai QZ, Liu JH, Chua CK. Influence of Ni content on microstructure of W-Ni alloy produced by selective laser melting. Int J Refract Metals Hard Mater 2014;45:15-22.

[260] Dadbakhsh S, Hao L. Effect of Al alloys on selective laser melting behaviour and microstructure of in situ formed particle reinforced composites. J Alloys Compd 2012;541:328-34.

[261] Wang Q, Zhao Z, Bai P, Wenbo Du, Haihong Liao, Yuxin Li. Effects of alloying elements X ( $\mathrm{Cr}, \mathrm{Mn}, \mathrm{Mo}, \mathrm{Ni}, \mathrm{Si})$ on the interface stability of $\mathrm{TiC}(001) / \gamma-\mathrm{Fe}(001)$ in $\mathrm{TiC} / 316 \mathrm{~L}$ stainless steel composite formed by selective laser melting: first principles and experiments. Adv Compos Hybrid Mater 2021;4:195-204.

[262] Wei K, Zeng X, Wang Z, Deng J, Liu M, Huang G, et al. Selective laser melting of Mg-Zn binary alloys: effects of Zn content on densification behavior, microstructure, and mechanical property. Mater Sci Eng, A 2019 May 22;756:226-36.

[263] Kimura T, Nakamoto T, Mizuno M, Araki H. Effect of silicon content on densification, mechanical and thermal properties of Al-xSi binary alloys fabricated using selective laser melting. Mater Sci Eng, A 2017 Jan 13;682:593-602.

[264] Wang XJ, Zhang LC, Fang MH, Sercombe TB. The effect of atmosphere on the structure and properties of a selective laser melted Al-12Si alloy. Mater Sci Eng 2014;597:370-5.

[265] Hauser C, Childs THC, Dalgarno KW, Eane RB. Atmospheric control during selective laser sintering of stainless steel 314S powder. In: Proceedings of the 10th solid freeform fabrication symposium. Austin, TX; 1999. p. 265-72.

[266] Schaffer GB, Hall BJ, Bonner SJ, Huo SH, Sercombe TB. The effect of the atmosphere and the role of pore filling on the sintering of aluminium. Acta Mater 2006;54(1):131-8.

[267] Wu X, Sharman R, Mei J, Voice W. Direct laser fabrication and microstructure of a burn-resistant Ti alloy. Mater Des 2002;23:239-47.

[268] Martin JM, Castro F. Liquid phase sintering of $\mathrm{P} / \mathrm{M}$ aluminium alloys: effect of processing conditions. J Mater Process Technol 2003;143-144:814-21.

[269] Steen W. Laser material processing-an overview. J Opt Pure Appl Opt 2003;5:S3.

[270] Do DK, Li P. The effect of laser energy input on the microstructure, physical and mechanical properties of Ti6Al-4V alloys by selective laser melting. Virtual Phys Prototyp 2016;11:41-7.

[271] Han J, Yang J, Yu H, Yin J, Gao M, Wang Z, et al. Microstructure and mechanical property of selective laser melted Ti6Al4V dependence on laser energy density. Rapid Prototyp J 2017;23:217-26.

[272] Steen WM. Laser materials processing. London: Springer; 2003.

[273] Basu B, Date AW. Rapid solidification following laser melting of pure metals-I. Study of flow field and role of convection. Int J Heat Mass Tran 1992;35(5):1049-58.

[274] Basu B, Date AW. Rapid solidification following laser melting of pure metals-II. Study of pool and solidification characteristics. Int J Heat Mass Tran 1992;35(5):1059-67.

[275] Loretto MH, Godfrey AB, Hu D, Blenkinsop PA, Jones IP, Cheng TT. The influence of composition and processing on the structure and properties of TiAl-based alloys. Intermetallics 1998;6(7-8):663-6.

[276] Srivastava D, Hu D, Chang ITH, Loretto MH. The influence of thermal processing route on the microstructure of some TiAl-based alloys. Intermetallics 1999;7(10):1107-12.

[277] Ion JC. Laser processing of engineering materials: principles, procedures and industrial applications. London: Elsevier Butterworth Heinemann; 2005.

[278] Shuai C, Yang Y, Wu P, Lin X, Liu Y, Zhou Y, et al. Laser rapid solidification improves corrosion behavior of $\mathrm{Mg}$ - $\mathrm{Zn}$ Zr alloy. J Alloys Compd 2017;691:961-9.

[279] Wang X, Gong X, Chou K. Review on powder-bed laser additive manufacturing of Inconel 718 parts. In: Proceedings of the ASME 2015 international manufacturing science and engineering conference, charlotte, NC, USA. vols. 8-12; June 2015.\\
[280] Cherry JA, Davies HM, Mehmood S, Lavery NP, Brown SG, Sienz J. Investigation into the effect of process parameters on microstructural and physical properties of 316L stainless steel parts by selective laser melting. Int J Adv Manuf Technol 2015 Feb;76(5):869-79.

[281] Song B, Dong S, Zhang B, Liao H, Coddet C. Effects of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V. Mater Des 2012 Mar 1;35:120-5.

[282] Bang GB, Kim WR, Kim HK, Park HK, Kim GH, Hyun SK, et al. Effect of process parameters for selective laser melting with SUS316L on mechanical and microstructural properties with variation in chemical composition. Mater Des 2021 Jan 1;197:109221.

[283] Pekok MA, Setchi R, Ryan M, Han Q Gu D. Effect of process parameters on the microstructure and mechanical properties of AA2024 fabricated using selective laser melting. Int J Adv Manuf Technol 2021 Jan;112(1):175-92.

[284] Kasperovich G, Hausmann J. Improvement of fatigue resistance and ductility of TiAl6V4 processed by selective laser melting. J Mater Process Technol 2015;220:202. 14.

[285] Rosenberg HW, Chesnutt JC, Margolin H. 1982 Fracture properties of titanium alloys. Appl. Fract. Mech. Sel. Met. Struct. Mater. 83-25317 10-26) Met. Park. OH, Am. Soc. Met. 1982:213-52.

[286] Wu SQ, Lu YJ, Gan YL, Huang TT, Zhao CQ, Lin JJX, et al. Microstructural evolution and microhardness of a selectivelaser-melted Ti-6Al-4V alloy after post heat treatments. J Alloys Compd 2016;672:643-52.

[287] Gil Mur FX, Rodríguez D, Planell J a. Influence of tempering temperature and time on the $\alpha^{\prime}$-Ti-6Al-4V martensite. J Alloys Compd 1996;234:287-9.

[288] Sallica-Leva E, Caram R, Jardini AL, Fogagnolo JB. Ductility improvement due to martensite $\alpha^{\prime}$ decomposition in porous Ti-6Al-4V parts produced by selective laser melting for orthopedic implants. J Mech Behav Biomed Mater 2016;54:149-58.

[289] Zhang H, Zhu H, Nie X, Qi T, Hu Z, Zeng X. In: Gu B, Helvajian H, Pique A, editors. Fabrication and heat treatment of high strength $\mathrm{Al}-\mathrm{Cu}-\mathrm{Mg}$ alloy processed using selective laser melting. San Francisco: E-Publishing, Inc.; 2016. 97380X1-7.

[290] Elgallads EM, Shen P, Zhang Z, Chen X. Mater Des 2014;61:133-40.

[291] King WE, Barth HD, Castillo VM, Gallegos GF, Gibbs JW, Hahn DE, et al. Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J Mater Process Technol 2014 Dec 1;214(12):2915-25.

[292] Tenbrock C, Fischer FG, Wissenbach K, Schleifenbaum JH, Wagenblast P, Meiners W, et al. Influence of keyhole and conduction mode melting for top-hat shaped beam profiles in laser powder bed fusion. J Mater Process Technol 2020 Apr 1;278:116514.

[293] Jadhav SD, Goossens LR, Kinds Y, Van Hooreweder B, Vanmeensel K. Laser-based powder bed fusion additive manufacturing of pure copper. Addit Manuf 2021 Jun 1;42:101990.

[294] Gargalis L, Ye J, Strantza M, Rubenchik A, Murray JW, Clare AT, et al. Determining processing behaviour of pure $\mathrm{Cu}$ in laser powder bed fusion using direct microcalorimetry. J Mater Process Technol 2021 Aug 1;294:117130.

[295] Chen Q, Zhao Y, Strayer S, Zhao Y, Aoyagi K, Koizumi Y, et al. Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment. Addit Manuf 2021 Jan 1;37:101642.

[296] Plaza L, Irisarri A, Gil-Negrete A. Improvement of Ti-6Al-4V fracture toughness by sub-transus heat treatment. Scripta Mater 1990;24.

[297] Pastor M, Zhao H, Martukanitz RP, Debroy T. Porosity, underfill and magnesium loss during continuous wave Nd:YAG laser welding of thin plates of aluminum alloys 5182 and 5754. Weld J 1999;78:207s-16s.

[298] Gu DD, Meiners W, Wissenbach K, Poprawe R. Laser additive manufacturing of metallic components: materials, processes and mechanisms. Int Mater Rev 2012;57:133-64.

[299] King WE, Anderson AT, Ferencz RM, Hodge NE, Kamath C, Khairallah SA, et al. Laser powder bed fusion additive manufacturing of metals; physics, computational, and materials challenges. Appl Phys Rev 2015;2:041304.

[300] Prashanth KG, Scudino S, Klauss HJ, Surreddi KB, Löber L, Wang Z, et al. Microstructure and mechanical properties of Al-12Si produced by selective laser melting: effect of heat treatment. Mater Sci Eng 2014;590:153-60.

[301] Liu YJ, Li SJ, Wang HL, Hou WT, Hao YL, Yang R, et al. Microstructure, defects and mechanical behavior of betatype titanium porous structures manufactured by electron beam melting and selective laser melting. Acta Mater 2016;113:56-67.

[302] Pauly S, Löber L, Petters R, Stoica M, Scudino S, Kühn U, et al. Processing metallic glasses by selective laser melting. Mater Today 2013;16:37-41.

[303] Kosiba K, Scudino S, Kobold R, Kühn U, Greer AL, Eckert J, et al. Transient nucleation in flash-annealed bulk metallic glasses. Acta Mater 2017;127:416-25.

[304] Hofmeister W, Griffith M, Ensz M, Smugeresky J. Solidification in direct metal deposition by LENS processing. JOM 2001;53:30-4.

[305] Tang M, Pistorius PC, Narra S, Beuth JL. Rapid solidification: selective laser melting of AlSi10Mg. JOM 2016;68:960-6.

[306] Kundin J, Mushongera L, Emmerich H. Phase-field modeling of microstructure formation during rapid solidification in Inconel 718 superalloy. Acta Mater 2015;95:343-56.

[307] Hu HW, Ding XP, Wang LZ. Numerical analysis of heat transfer during multilayer selective laser melting of AlSi10Mg. Optik 2016;127:8883-91.

[308] Liu YJ, Liu Z, Jiang Y, Wang GW, Yang Y, Zhang LC. Gradient in microstructure and mechanical property of selective laser melted AlSi10Mg. J Alloys Compd 2018;735:1414-21.

[309] Bertoli US, Wolfer AJ, Matthews MJ, Delplanque JPR, Schoenung JM. On the limitations of volumetric energy density as a design parameter for selective laser melting. Mater Des 2017;113:331-40.

[310] Prashanth KG, Scudino S, Maity T, Das J, Eckert J. Is the energy density a reliable parameter for materials synthesis by selective laser melting? Materials Research Letters 2017;5:386-90.

[311] Pauly S, Wang P, Kuhn U, Kosiba K. Experimental determination of cooling rates in selectively laser-melted eutectic Al-33Cu, Additive Manufacturing. 2018. https:// \href{http://doi.org/10.1016/j.addma.2018.05.034}{doi.org/10.1016/j.addma.2018.05.034}.

[312] Griffith ML, Schlienger ME, Harwell LD, Oliver MS, Baldwin MD, Ensz MT, et al. Understanding thermal behavior in the LENS process. Mater Des 1999;20:107-13.

[313] Zheng B, Zhou Y, Smugeresky JE, Schoenung JM, Lavernia EJ. Thermal behavior and microstructure evolution during laser deposition with laserengineered net shaping: Part II. Experimental investigation and discussion, Metallurgical and Materials Transactions A 2008;39:2237-45.

[314] Dai DH, Gu DD, Poprawe R, Xia MJ. Influence of additive multilayer feature on thermodynamics, stress and microstructure development during laser 3D printing of aluminum-based material. Sci Bull 2017;62:779-87.\\
[315] Li XP, Wang XJ, Saunders M, Suvorova A, Zhang LC, Liu YJ, et al. A selective laser melting and solution heat treatment refined Al-12Si alloy with a controllable ultrafine eutectic microstructure and 25\% tensile ductility. Acta Mater 2015;95:74-82.

[316] Casavola C, Campanelli S, Pappalettere C. Preliminary investigation on distribution of residual stress generated by the selective laser melting process. J Strain Anal Eng Des 2009;44:93-104.

[317] Shamsaei N, Yadollahi A, Bian L, Thompson SM. An overview of Direct Laser Deposition for additive manufacturing; Part II: mechanical behavior, process parameter optimization and control. Addit Manuf 2015;8:12-35.

[318] Kruth J-P, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M, Lauwers B. Selective laser melting of ironbased powder. J Mater Process Technol 2004;149:616-22.

[319] Spierings AB, Herres N, Levy G. Influence of the particle size distribution on surface quality and mechanical properties in AM steel parts. Rapid Prototyp J 2011;17(3):195-202.

[320] Spierings AB, Levy G. Comparison of density of stainless steel 316L parts produced with selective laser melting using different powder grades. In: Proceedings of the 20th solid freeform fabrication symposium. Austin, Texas; 2009. p. 324-53.

[321] Buchbinder D, Schleifenbaum H, Heidrich S, Meiners W, Bültmann J. High power selective laser melting (HP SLM) of aluminium parts. In: Physics procedia of the sixth international WLT conference on lasers in manufacturing. 12A. Germany: Munich ICM; 2012. p. 271-8.

[322] Buchbinder D, Meiners W, Wissenbach K, MüllerLohmeier K, Brandl E. Rapid manufacturing of aluminum parts for serial production via selective laser melting (SLM). In: Hirsch J, editor. Aluminum alloys, vol. 2. Wiley; 2008. p. 2394-400.

[323] Mercelis P, Kruth JP. Residual stresses in selective laser sintering and selective laser melting. Rapid Prototyp J 2006;12(5):254-65.

[324] Gu DD, Meiners W. Microstructure characteristics and formation mechanisms of in situ WC cemented carbide based hard metals prepared by selective laser melting. Mater Sci Eng 2010;527:7585-92.

[325] Parande G, Manakari V, Meenashisundaram GK, Gupta M. Enhancing the hardness/compression/damping response of magnesium by reinforcing with biocompatible silica nanoparticulates. Int J Mater Res 2016;107:1091-9.

[326] Jain A, Basu B, Kumar BVM, Harshavardhan, Sarkar J. Grain size-wear rate relationship for titanium in liquid nitrogen environment. Acta Mater 2010;58:2313-23.

[327] Carlton HD, Haboub A, Gallegos GF, Parkinson DY, McDowell A. Damage evolution and failure mechanism in additively manufactured strainless steel. Mater Sci Eng 2016;651:406-14.

[328] Ma E. Eight routes to improve the tensile ductility of bulk nanostructured metals and alloys. JOM 2006;58:49-53.

[329] Gu D, Wang H, Dai D, Yuan P, Meiners W, Poprawe R. Rapid fabrication of Al-based bulk-form nanocomposites with novel reinforcement and enhanced performance by selective laser melting. Scripta Mater 2015;96:25-8.

[330] Kobryn PA, Moore EH, Semiatin SL. The effect of laser power and traverse speed on microstructure, porosity, and build height in laser-deposited Ti-6Al-4V. Scripta Mater 2000;43:299-305.

[331] Kobryn PA, Semiatin SL. Mechanical properties of laserdeposited Ti-6Al-4V. JOM 2006:179-86.

[332] Wang Y, Bergstrom J, Burman C. Characterisation of an iron-based laser sintered material. J Mater Process Technol 2006;172:77-87.

[333] Wang Y, Bergstrom J, Burman C. Four-point bending fatigue behaviour of an iron-based laser sintered material. Int J Fatig 2006;28(12):1705-15.

[334] Bassoli E, Sewell N, Denti L, Gatto A. Investigation into the failure of Inconel exhaust collector produced by laser consolidation. Eng Fail Anal 2013;35:397-404.

[335] Chawla N, Andres C, Jones JW, Allison JE. The effect of reinforcement volume fraction and particle size on the fatigue behavior of an aluminum alloy/SiC composite. Ind Heat 1999;66:61-6.

[336] Yang KV, Rometsch P, Jarvis T, Rao J, Cao S, Davies C, et al. Porosity formation mechanisms and fatigue response in AlSi-Mg alloys made by selective laser melting. Mater Sci Eng 2018;712:166-74.

[337] Tang M, Pistorius PC. Oxides, porosity and fatigue performance of AlSi10Mg parts produced by selective laser melting. Int J Fatig 2017;94:192-201.

[338] Edwards P, Ramulu M. Fatigue performance evaluation of selective laser melted Ti-6Al-4V. Mater Sci Eng 2014;598:327-37.

[339] Ammar HR, Samuel AM, Samuel FH. Porosity and the fatigue behaviour of hypoeutectic and hypereutectic aluminum - silicon casting alloys. Int J Fatig 2008;30:1024-35.

[340] Qian G, Hong Y, Zhou C. Investigation of high cycle and very-high-cycle fatigue behaviours for a structural steel with smooth and notched specimens. Eng Fail Anal 2011;17:1517-25

[341] Bourell D, Marcus HL, Barlow JW, Beaman JJ. Selective laser sintering of metals and ceramics. Int J Powder Metall 1992;28:369-81.

[342] Yap CY, Chua CK, Dong Z, Liu Z, Zhang D, Loh LE, et al. Journal of Applied Physics Rev 2015;2:518. 187.

[343] Gu D, Shen Y. Balling phenomena in direct laser sintering of stainless-steel powder: metallurgical mechanisms and control methods. Mater Des 2009;30:2903-10.

[344] Zhang X, Liu X, Zhang D, Shen Z, Liu W. Balling phenomena in selective laser melted tungsten. J Mater Process Technol 2015;222:33-42.

[345] Shiomi M, Osakada K, Nakamura K, Yamashita T, Abe F. Residual stress within metallic model made by selective laser melting process. CIRP Annals 2004;53(Issue 1):195-8.

[346] Shi X, Ma S, Liu C, Wu Q. Parameter optimization for Ti$47 \mathrm{Al}-2 \mathrm{Cr}-2 \mathrm{Nb}$ in selective laser melting based on geometric characteristics of single scan tracks. Opt Laser Technol 2017;90:71-9.

[347] Yang J, Han J, Yu H, Yin J, Gao M, Wang Z, et al. Role of molten pool mode on formability, microstructure and mechanical properties of selective laser melted Ti-6Al-4V alloy. Mater Des 2016;110:558-70.

[348] Liu Y, Yang Y, Mai S, Wang D, Song C. Investigation into spatter behavior during selective laser melting of AISI 316L stainless steel powder. Mater Des 2015;87:797-806.

[349] Gu D, Shen Y. Balling phenomena during direct laser sintering of multi-component Cu-based metal powder. J Alloys Compd 2007;432:163-6.

[350] Jia Qingbo, Gu Dongdong. Selective laser melting additive manufacturing of Inconel 718 superalloy parts: densification, microstructure and properties. J Alloys Compd 2014;585(5 February):713-21.

[351] Dai Donghua, Gu Dongdong. Thermal behavior and densification mechanism during selective laser melting of copper matrix composites: simulation and experiments. Mater Des March 2014;55:482-91.

[352] Xiao R, Zhang X. Problems and issues in laser beam welding of aluminum-lithium alloys. J Manuf Process 2014;16:166-75.\\
[353] Read N, Wang W, Essa K, Attallah MA. Selective laser melting of AlSi10Mg alloy: process optimisation and mechanical properties development. Mater Des 2015;65:417-24.

[354] Xia M, Gu D, Yu G, Dai D, Chen H, Shi Q. Porosity evolution and its thermodynamic mechanism of randomly packed powder-bed during selective laser melting of Inconel 718 alloy. Int J Mach Tool Manufact 2017;116:96-106.

[355] Monroy K, Delgado J, Ciurana J. Study of the pore formation on CoCrMo alloys by selective laser melting manufacturing process. Procedia Eng 2013;63:361-9.

[356] Fu N Kang, H Liao, Y Gao, C Coddet. An investigation on selective laser melting of $\mathrm{Al}-\mathrm{Cu}-\mathrm{Fe}-\mathrm{Cr}$ quasicrystal: from single layer to multilayers. Intermetallics 2017;86:51-8.

[357] King WE, Barth HD, Castillo VM, Gallegos GF, Gibbs JW, Hahn DE, et al. Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing. J Mater Process Technol December 2014;214(Issue 12):2915-25.

[358] Dai D, Gu D. Effect of metal vaporization behavior on keyhole-mode surface morphology of selective laser melted composites using different protective atmospheres. Appl Surf Sci 2015;355:310-9.

[359] Zhao X, Li S, Zhang M, Liu Y, Sercombe TB, Wang S, et al. Comparison of the microstructures and mechanical properties of Ti-6Al- $4 \mathrm{~V}$ fabricated by selective laser melting and electron beam melting. Mater Des 5 April 2016;95:21-31.

[360] Zheng B, Topping T, Smugeresky JE, Zhou Y, Biswas A, Baker D, et al. The influence of Ni-coated TiC on laserdeposited IN625 metal matrix composites. Metall Mater Trans 2010;41:568-73.

[361] Cao X, Wallace W, Poon C, Immarigeon JP. Mater. Manuf. Progress 2003;18:23-49.

[362] Kaufmann N, Imran M, Wischeropp TM, Emmelmann C, Siddique S, Walther F. Influence of process parameters on the quality of aluminium alloy EN AW 7075 using selective laser melting (SLM). Phys Procedia 2016;83:918-26.

[363] Weingarten C, Buchbinder D, Pirch N, Meiners W, Wissenbach K, Poprawe R. Formation and reduction of hydrogen porosity during selective laser melting of AlSi10Mg. J Mater Process Technol 2015;221:112-20.

[364] Qiu C, Panwisawas C, Ward M, Basoalto HC, Brooks JW, Attallah MM. On the role of melt flow into the surface structure and porosity development during selective laser melting. Acta Mater 2015;96:72-9.

[365] Li Y, Yang H, Lin X, Huang W, Li J, Zhou Y. The influences of processing parameters on forming characterizations during laser rapid forming. Mater Sci Eng, A 2003;360(1):18-25.

[366] Lee Y, Zhang W. Mesoscopic simulation of heat transfer and fluid flow in laser powder bed additive manufacturing. In: International solid free form fabrication symposium. Austin; 2015. p. 1154-65.

[367] Mumtaz K, Hopkinson N. Top surface and side roughness of Inconel 625 parts processed using selective laser melting. Rapid Prototyp J 2009;15(2):96-103.

[368] Resch M, Kaplan AFH, Schuoecker D. Laser-assisted generating of three-dimensional parts by the blown powder process. International Symposium on Gas Flow and Chemical Lasers and High-Power Laser Conference 2001;4184:555-8.

[369] de Formanoir C, Michotte S, Rigo O, Germain L, Godet S. Electron beam melted Ti-6Al-4V: microstructure, texture and mechanical behavior of the as-built and heat-treated material. Mater Sci Eng, A 2016;652:105-19.

[370] Shaikh JH, Jain NK, Venkatesh VC. Precision finishing of bevel gears by electrochemical honing. Mater Manuf Process 2013;28(10):1117-23.

[371] Zhang J, Song B, Wei Q, Bourell D, Shi Y. A review of selective laser melting of aluminum alloys: processing, microstructure, property and developing trends. J Mater Sci Technol 2018;35(2):270-84.

[372] Sing SL, An J, Yeong WY, Wiria FE. Laser and electron-beam powder-bed additive manufacturing of metallic implants: a review on processes, materials and designs. J Orthop Res 2016;34:369-85.

[373] Chen CM, Kovacevic R. Finite element modeling of friction stir welding-thermal and thermomechanical analysis. Int J Mach Tool Manufact 2003;43:1319-26.

[374] Deng D, Murakawa H. Numerical simulation of temperature field and residual stress in multi-pass welds in stainless steel pipe and comparison with experimental measurements. Comput Mater Sci 2006;37:269-77.

[375] Kim D, Hwang J, Kim E, Heo Y, Woo W, Choi SH. Evaluation of the stress-strain relationship of constituent phases in AlSi10Mg alloy produced by selective laser melting using crystal plasticity FEM. Journal of Alloys \& Compdounds 2017;714:687-97.

[376] Lindgren LE. Numerical modelling of welding. Comput Methods Appl Mech Eng 2006;195:6710-36.

[377] Ouyang D, Li N, Xing W, Zhang J, Liu L. 3D printing of crackfree high strength Zr-based bulk metallic glass composite by selective laser melting. Intermetallics 2017;90:128-34.

[378] Kou S. Welding metallurgy. 2nd ed. Hoboken, NJ: John Wiley \& Sons; 2003.

[379] Carter LN, Attallah MM, Reed RC. Laser powder bed fabrication of nickel-base superalloys: influence of parameters; characterisation, quantification and mitigation of cracking. Superalloys 2012;2012:577-86.

[380] Liu Y, Yang Y, Wang D. A study on the residual stress during selective laser melting (SLM) of metallic powder. Int J Adv Manuf Technol 2016;87:647-56.

[381] Cao X, Jahazi M, Immarigeon J, Wallace W. A review of laser welding techniques for magnesium alloys. J Mater Process Technol 2006;171:188-204.

[382] Ramasamy S, Albright C. CO2 and Nd: YAG laser beam welding of 6111-T4 aluminum alloy for automotive applications. J Laser Appl 2000;12:101-15.

[383] Zhao H, DebRoy T. Weld metal composition change during conduction mode laser welding of aluminum alloy 5182. Metall Mater Trans B 2001;32:163-72.

[384] Collur MM, Paul A, DebRoy T. Mechanism of alloying element vaporization during laser welding. Metall Mater Trans B 1987;18:733-40.

[385] Klassen A, Forster VE, Juechter V, Körner C. Numerical simulation of multi-component evaporation during selective electron beam melting of TiAl. J Mater Process Technol 2017;247:280-8.

[386] Mao Z, Zhang D, Wei P, Zhang K. Manufacturing feasibility and forming properties of Cu-4Sn in selective laser melting. Materials 2017;10:333.

[387] Olakanmi E, Cochrane R, Dalgarno K. A review on selective laser sintering/melting (SLS/SLM) of aluminium alloy powders: processing, microstructure, and properties. Prog Mater Sci 2015;74:401-77.

[388] Campbell J. Casting. Oxford: Butterworth-Heinemann; 1991.

[389] Dunkley JJ. Atomisation. In: Davis RJ, editor. Powder metal technologies and applications. Materials park (OH): ASM international handbook committee. ASM International; 1998. p. 35-52.

[390] Morgan R, Papworth A, Sutcliffe C, Fox P, O'Neill W. High density net shape components by direct laser re-melting of single-phase powders. J Mater Sci 2002;37:3093-100.

[391] Dalgarno KW, Wright CS. Approaches to processing metals and ceramics through the laser scanning of powder beds a review. Powder Metallurgy Progress 2001;1(1):70-9.\\
[392] O'Neill W, Sutcliffe CJ, Morgan R, Landsborough A, Hon KKB. Investigation on multi-layer direct metal laser sintering of 316L stainless steel powder beds. CIRP Annals 1999;48(1):151-4.

[393] Mills KC, Keene BJ, Brooks RF, Shirali A. Marangoni effects in welding. In: Hondros ED, McLean M, Mills KC, editors. Marangoni and interfacial phenomena in materials processing. The Royal Society London: Institute of Materials, Minerals and Mining; 1998. p. 97-111.

[394] Romano S, Brandão A, Gumpinger J, Gschweitl M, Beretta S. Qualification of AM parts: extreme value statistics applied to tomographic measurements. Mater Des 2017 Oct 5;131:32-48.

[395] Romano S, Abel A, Gumpinger J, Brandão AD, Beretta S. Quality control of AlSi10Mg produced by SLM: metallography versus CT scans for critical defect size assessment. Addit Manuf 2019 Aug 1;28:394-405.

[396] Bao J, Wu S, Withers PJ, Wu Z, Li F, Fu Y, et al. Defect evolution during high temperature tension-tension fatigue of SLM AISi10Mg alloy by synchrotron tomography. Mater Sci Eng, A 2020 Aug 5;792:139809.

[397] Patriarca L, Beretta S, Foletti S, Riva A, Parodi S. A probabilistic framework to define the design stress and acceptable defects under combined-cycle fatigue conditions. Eng Fract Mech 2020 Feb 1;224:106784.

[398] Wu SC, Xiao TQ, Withers PJ. The imaging of failure in structural materials by synchrotron radiation X-ray microtomography. Eng Fract Mech 2017 Sep 1;182:127-56.

[399] Hu YN, Wu SC, Withers PJ, Zhang J, Bao HY, Fu YN, et al. The effect of manufacturing defects on the fatigue life of selective laser melted Ti-6Al-4V structures. Mater Des 2020 Jul 1;192:108708.

[400] Wu Z, Wu S, Bao J, Qian W, Karabal S, Sun W, et al. The effect of defect population on the anisotropic fatigue resistance of AlSi10Mg alloy fabricated by laser powder bed fusion. Int J Fatig 2021 Oct 1;151:106317.

[401] Bao J, Wu Z, Wu S, Withers PJ, Li F, Ahmed S, et al. Hot dwell-fatigue behaviour of additively manufactured AlSi10Mg alloy: relaxation, cyclic softening and fracture mechanisms. Int J Fatig 2021 Oct 1;151:106408.

[402] Zhang J, Li J, Wu S, Zhang W, Sun J, Qian G. High-cycle and very-high-cycle fatigue lifetime prediction of additively manufactured AlSi10Mg via crystal plasticity finite element method. Int J Fatig 2022 Feb 1;155:106577.

[403] Hu YN, Wu SC, Wu ZK, Zhong XL, Ahmed S, Karabal S, et al. A new approach to correlate the defect population with the fatigue life of selective laser melted Ti-6Al-4V alloy. Int J Fatig 2020 Jul 1;136:105584.

[404] Qian W, Wu S, Wu Z, Ahmed S, Zhang W, Qian G, et al. In situ X-ray imaging of fatigue crack growth from multiple defects in additively manufactured AlSi10Mg alloy. Int J Fatig 2022 Feb 1;155:106616.

[405] Ravi P, Naragani D, Kenesei P, Park JS, Sangid MD. Direct observations and characterization of crack closure during microstructurally small fatigue crack growth via in-situ high-energy X-ray characterization. Acta Mater 2021 Feb 15;205:116564.

[406] Choo H, White LP, Xiao X, Sluss CC, Morin D, Garlea E. Deformation and fracture behavior of a laser powder bed fusion processed stainless steel: in situ synchrotron x-ray computed microtomography study. Addit Manuf 2021 Apr 1;40:101914.

[407] Hu Y, Ao N, Wu S, Yu Y, Zhang H, Qian W, et al. Influence of in situ micro-rolling on the improved strength and ductility of hybrid additively manufactured metals. Eng Fract Mech 2021 Aug 1;253:107868.

[408] Nezhadfar PD, Thompson S, Saharan A, Phan N, Shamsaei N. Structural integrity of additively manufactured\\
aluminum alloys: effects of build orientation on microstructure, porosity, and fatigue behavior. Addit Manuf 2021 Nov 1;47:102292.

[409] Xie C, Wu S, Yu Y, Zhang H, Hu Y, Zhang M, et al. Defectcorrelated fatigue resistance of additively manufactured AlMg4. 5Mn alloy with in situ micro-rolling. J Mater Process Technol 2021 May 1;291:117039.

[410] Shiva S, Yadaiah N, Palani IA, Paul CP, Bindra KS. Thermo mechanical analyses and characterizations of TiNiCu shape memory alloy structures developed by laser additive manufacturing. J Manuf Process 2019;48:98-109.

[411] Mishra AK, Kumar A. Numerical and experimental analysis of the effect of volumetric energy absorption in powder layer on thermal-fluidic transport in selective laser melting of Ti6Al4V. Opt Laser Technol 2019;111:227-39.

[412] Francois MM, Sun A, King WE, Henson NJ, Tourret D, Bronkhorst CA, et al. Modeling of additive manufacturing processes for metals: challenges and opportunities. Curr Opin Solid State Mater Sci 2017;21:198-206. \href{https://doi.org/}{https://doi.org/} 10.1016/j.cossms.2016.12.001.

[413] Bikas H, Stavropoulos P, Chryssolouris G. Additive manufacturing methods and modeling approaches: a critical review. Int J Adv Manuf Technol 2016;83:389-405. \href{https://doi.org/10.1007/s00170-015-7576-2}{https://doi.org/10.1007/s00170-015-7576-2}.

[414] Schelmetic T. GENOA 3DP additive manufacturing simulation tool updated to add metal AM. Design News; 2018.

[415] Conti P, Cianetti F, Pilerci P. Parametric finite element model of SLM additive mnufacturing process. Procedia Struct Integr 2018;8:410-21.

[416] Hussein A, Hao L, Yan C, Everson R. Finite element simulation of the temperature and stress fields in single layers built without-support in selective laser melting. Mater Des 2013;52:638-47.

[417] Huang W, Zhang Y. Finite element simulation of thermal behavior in single-track multiple-layers thin wall withoutsupport during selective laser melting. J Manuf Process 2019;42:139-48.

[418] Khan HM, Dirikolu MH, Koc E, Oter ZC. Numerical investigation of heat current study across different platforms in SLM processed multi-layer AlSi10Mg. Optik International Journal for Light and Electron Optics 2018;170:82-9.

[419] Baere DD, Bayat M, Mohanty S, Hattel J. Thermo-fluidmetallurgical modelling of the selective laser melting process chain. Procedia CIRP 2018;74:87-91.

[420] Leitz KH, Singer P, Plankensteiner A, Tabernig B, Kestler H, Sigl LS. Multi-physical simulation of selective laser melting. Metal Powder Report; 2016.

[421] Leitz KH, Grohs C, Singer P, Tabernig B, Plankensteiner A, Kestler H, et al. Fundamental analysis of the influence of powder characteristics in Selective Laser Melting of molybdenum based on a multi-physical simulation model. Int J Refract Metals Hard Mater 2018;72:1-8.

[422] Tan P, Shen F, Li B, Zhou K. A thermo-metallurgicalmechanical model for selective laser melting of Ti6Al4V. Mater Des 2019;168.

[423] Majeed M, Khan HM, Rasheed I. Finite element analysis of melt pool thermal characteristics with passing laser in SLM process. Optik - International Journal for Light and Electron Optics 2019;194.

[424] Luo Z, Zhao YF. Finite element thermal analysis of melt pool in selective laser melting process. In: Proceedings of the ASME 2018 international design engineering; 2018.

[425] Schanzel M, Shakiriv D, Ilin A, Ploshikhin V. Coupled thermo-mechanical process simulation method forselective laser melting considering phase transformation steels. Comput Math Appl 2019;78(7):2230-46.\\
[426] Li Y, Zhou K, Tan P, Tor SB, Chua CK, Leong KF. Modelling temperature and residual stress pelds in selective laser melting. Int J Mech Sci 2017. \href{https://doi.org/10.1016/}{https://doi.org/10.1016/} j.ijmecsci.2017.12.001.

[427] Pitassi E, Savoia V, Fontanari A, Molinari V, Luchin Pitassi D, et al. Finite element thermal analysis of metal parts additively manufactured via selective laser melting. Finite Element Method-Simulation, Numerical Analysis and Solution Techniques 2018. pp. 613-318.Vancouver.

[428] Olleak A, Xi Z. Simulation of layer-by-layer selective laser melting process with an efficient remeshing technique. Procedia Manuf 2019;34. 613-318.

[429] Kovaleva I, Kovalev O, Smurov I. Model of heat and mass transfer in random packing layer of powder particles in selective laser melting. Phys Procedia 2014;56:400-10.

[430] Ahmad B, der Veen SO, Fitzpatrick ME, Guo H. Residual stress evaluation in selective-laser-melting additively manufactured titanium (Ti- 6Al-4V) and Inconel 718 using the contour method and numerical simulation. Addit Manuf 2018;22:571-82.

[431] Jhabvala J, Boillat E, Antignac T, Glardon R. On the effect of scanning strategies in the selective laser melting process. Virtual Phys Prototyp 2010;5:99-109.

[432] Mohantya S, Hattel JH. Numerical model-based reliability estimation of selective laser melting process. Phys Procedia 2014;56:379-89.

[433] Loh L, Chua C, Yeong W, Song J, Mapar M, Sing S, et al. Numerical investigation and an effective modelling on the Selective Laser Melting (SLM) process with aluminium alloy 6061. Int J Heat Mass Tran 2015;80:288-300.

[434] Ho JY, Wong KK, Leong KC, Wong TN. Convective heat transfer performance of airfoil heat sinks fabricated by selective laser melting. Int J Therm Sci 2017;114.

[435] Gaikwad A, Giera B, Guss GM, Forien JB, Matthews MJ, Rao P. Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion-a single-track study. Addit Manuf 2020 Dec 1;36:101659.

[436] Sing SL, Kuo CN, Shih CT, Ho CC, Chua CK. Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing. Virtual Phys Prototyp 2021 May 4;16(3):372-86.

[437] Okaro IA, Jayasinghe S, Sutcliffe C, Black K, Paoletti P, Green PL. Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit Manuf 2019 May 1;27:42-53.

[438] Ogoke F, Farimani AB. Thermal control of laser powder bed fusion using deep reinforcement learning. Addit Manuf 2021 Oct 1;46:102033.

[439] Baumgartl H, Tomas J, Buettner R, Merkel M. A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring. Progress in Additive Manufacturing 2020 Sep;5(3):277-85.

[440] Scime L, Beuth J. Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 2019 Jan 1;25:151-65.

[441] Sanchez S, Rengasamy D, Hyde CJ, Figueredo GP, Rothwell B. Machine learning to determine the main factors affecting creep rates in laser powder bed fusion. J Intell Manuf 2021 Dec;32(8):2353-73.

[442] Zhang Y, Yang S, Dong G, Zhao YF. Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model. Addit Manuf 2021 May 1;41:101946.

[443] Liu Q, Wu H, Paul MJ, He P, Peng Z, Gludovatz B, et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: new microstructure description\\
indices and fracture mechanisms. Acta Mater 2020 Dec 1;201:316-28.

[444] Peng X, Wu S, Qian W, Bao J, Hu Y, Zhan Z, et al. The potency of defects on fatigue of additively manufactured metals. Int J Mech Sci 2022 May 1;221:107185.

[445] Zhang M, Sun CN, Zhang X, Goh PC, Wei J, Hardacre D, et al. High cycle fatigue life prediction of laser additive manufactured stainless steel: a machine learning approach. Int J Fatig 2019 Nov 1;128:105194.

[446] Bao H, Wu S, Wu Z, Kang G, Peng X, Withers PJ. A machinelearning fatigue life prediction approach of additively manufactured metals. Eng Fract Mech 2021 Feb 1;242:107508.

[447] Moon S, Ma R, Attardo R, Tomonto C, Nordin M, Wheelock P, et al. Impact of surface and pore characteristics on fatigue life of laser powder bed fusion Ti-6Al-4V alloy described by neural network models. Sci Rep 2021 Oct 14;11(1):1-7.

[448] Hassanin H, Zweiri Y, Finet L, Essa K, Qiu C, Attallah M. Laser powder bed fusion of Ti-6Al-2Sn-4Zr-6Mo alloy and properties prediction using deep learning approaches. Materials 2021;14:2056. \href{https://doi.org/10.3390/ma14082056}{https://doi.org/10.3390/ma14082056}.

Dr. Sohini Chowdhury (Orcid ID: 0000-0003-4802-3722) has finished her Doctoral studies in December 2020 in Department of Mechanical Engineering at North Eastern Regional Institute of Science and Technology, Nirjuli, India. She also finished her postgraduation (M.Tech.-masters) in the same institution and department and completed with GOLD MEDAL in 2016. Her research area include laser based additive manufacturing technologies, FEM, Computational modeling of manufacturing processes etc. She published over 18 research articles in the peerreviewed international journals, conference proceedings, and book chapters. Google Scholar Profile: \href{https://scholar.google}{https://scholar.google}. \href{http://co.in/citations?user=1Vc_VxwAAAAJ&hl=en&authuser=1;}{co.in/citations?user=1Vc\_VxwAAAAJ\&hl=en\&authuser=1;} ResearchGate: \href{https://www.researchgate.net/profile/SohiniChowdhury-2}{https://www.researchgate.net/profile/SohiniChowdhury-2}

Dr. Yadaiah Nirsanametla (ORCID Id: 0000-0002-8316-8188) is currently working as Senior Grade Assistant Professor in the Department of Mechanical Engineering at North Eastern Regional Institute of Science and Technology (since February 2015), Nirjuli, India. More details can be found in the following links: Google Scholar Profile: \href{https://scholar.google.co.in/citations}{https://scholar.google.co.in/citations}? hl=en\&user=hb1lMJoAAAAJ; ResearchGate: \href{https://www}{https://www}. \href{http://researchgate.net/profile/Yadaiah-Nirsanametla;}{researchgate.net/profile/Yadaiah-Nirsanametla;} Institute's Homepage: \href{https://nerist.ac.in/mechanical/faculties/yadaiah}{https://nerist.ac.in/mechanical/faculties/yadaiah}

Chander Prakash:- Dr. Prakash working as a Professor in the School of Mechanical Engineering, Lovely Professional University, Punjab, India. His area of research is synthesis/development, surface modification, and advanced/precision machining of metallic and non-metallic biomaterials. Synthesis and development of magnesium-based biodegradable and titanium-based alloys and composites, respectively, using innovative manufacturing techniques such as spark plasma sintering, electrospinning and 3D printing. Surface modification of polymer, biodegradable-magnesium, and titanium-based biomaterials by electro-deposition, plasma spray deposition, and friction stir processing etc. Precise and advanced machining of biomaterials using electric discharge machining, magnetic abrasive finishing, and diamond turning processes. Dr. PRAKASH has authored more than 180 research articles (among them >95 SCI indexed research article) in the journals, conference proceedings, and books ( $\mathrm{H}-$ index 31, i10-index 84, Google Scholars citation 3185). In 2018 and 2019 he received the Research Excellence Award for publishing the highest number of publications at the University. He has edited 23 books and 3 authored books for various reputed publisher like Springer, Elsevier, CRC Press, and World Scientific. He is series editor of book "Sustainable Manufacturing Technologies: Additive, Subtractive, and Hybrid', CRC Press Taylor \& Francis, where more than 25 edited books were published by national and international researchers. He is serving editorial board member of peer reviewer intranational journal "Cogent Engineering" and "Frontiers in Manufacturing Technology". He is serving Guest Editor of peer reviewed SCI-indexed Journals.

Prof. Seeram Ramakrishna (0000-0001-8479-8686), FREng has more than 1750 publications in the domain of Material Science, Biomedical Engineering, Polymer Technology, Advance Manufacturing, Additive Manufacturing, and Sustainability. Here are top 5 list of publications included for ready reference: 1. A review on polymer nanofibers by electrospinning and their applications in nanocomposites, Composites Science and Technology, 63 (2003), 2223-2253 (Total citation: 8103). 2. Electrospinning of nano/micro scale poly (L-lactic acid) aligned fibers and their potential in neural tissue engineering, Biomaterials, 26 (2005), 2603-2610 (Total citation: 1935). 3. Biomedical applications of polymer-composite materials: a review, Composites Science and Technology 61 (2001), 1189-1224 (Total citation: 1504). 4. Aligned biodegradable nanofibrous structure: a potential scaffold for blood vessel engineering, Biomaterials 25 (2004), 877-886 (Total citation: 1395). 5. Potential of nanofiber matrix as tissue-engineering scaffolds, Tissue engineering, 11 (2005), 101-109 (Total citation: 1136). Please refer to the google scholar profile to review complete list of publications: \href{https://scholar.google.com/citations}{https://scholar.google.com/citations}? user $=a 49$ NVmkAAAAJ\&hl $=$ en

Prof. Saurav Dixit is working as a Postdoc fellow at "Peter the Great St. Petersburg Polytechnic University”, St Petersburg, Russia. His area of research is synthesis/development, construction productivity, self-healing concrete, chemical and physical properties of fly-ash, advance concrete production, additive manufacturing, and surface modification. He won the award for "Best Leadership Qualities" (2015) at Amity University Uttar Pradesh, India. Dr. Saurav has authored more than 57 research articles (among them $>15$ SCI indexed research article) in the journals, conference proceedings, and books (H-index 14, i10-index 19, Google Scholars citation 533). He has 3 authored books for various reputed publisher like Springer, Elsevier, CRC Press, and World Scientific. He is serving as an active reviewer and editorial board member for several scientific international journals that include.

Professor Dharam Buddhi, Vice chancellor, Uttaranchal University Dehradun India, an alumnus of IIT Delhi (Ph D; Energy Systems) and Ex-Professor, School of Energy and Environmental Studies, Devi Ahilya University, Indore and became University full Professor in the year 2000. Dr Buddhi has also worked in industry as Director (Technical), Chief Technical Advisor \& Head R \& D and also transferred the technology. His areas of Research are materials science, Renewable Energy, Energy Conservation, Energy Storage and Green Buildings. Recently, Dr Buddhi has been listed in $2 \%$ globally top researches by Elsevier and Stanford University USA. He has been International consultant to Asian Development Bank during 2016-2018 for Green Power project in Sri Lanka. Dr Buddhi is Ex-Vice Chancellor, Suresh Gyan Vihar University, Jaipur. Presently, Dr Buddhi is Indian Coordinator of Indo-Australia collaboration Project on "Thermal Energy Storage for Food/Grain Drying with CST/RE to Lower Pollution". He was member of 'Program Advisory Committee', on Technology System Development, Department of Science and Technology, GOI. Professor Buddhi had been Visiting Professor at UPC Barcelona Spain, Mie University Japan, Kun Shan University Tainan, Taiwan and Auckland University New Zealand. Dr Buddhi was on the Editorial Board of "Energy Conversion and Management", an Elsevier International Journal and is Reviewer of severally globally renowned Research Journal and a member of numerous professional bodies. He has\\
guided more than $90 \mathrm{M}$. Tech students theses. He guided 16 research scholars leading to Ph.D. degree in the field of Heat Transfer, Solar Energy, Green Buildings, Thermal Load Management, Fuel Cell, Cold Chain Solutions \& RAC and has published/ presented more than 126 research papers and reviews and 93 patents to his credit out of which a few are granted. He has the credit of first patent granted at Devi Ahilya University, Indore. Dr Buddhi has international academic and industrial exposure of U.K, Portugal, Germany, Italy, Spain, France, Switzerland, Japan, Korea, New Zealand, Taiwan, Sri Lanka, Australia and US. Dr Buddhi has very high citations of the order of $11,000+$ of his publications. As per the report of Government of India, International Comparative Research Base (2009-14) by Department of Science \& Technology, Top 10 publications in Energy, Dr Buddhi's paper was ranked number one.

Lovi Raj Gupta: Prof. Gupta is working as Pro-Vice chancellor, Lovely Professional University, Phagwara, India. He holds a PhD in Bioinformatics. He did his M.Tech. in Computer Aided Design \& Interactive Graphics from IIT, Kanpur and B. E. (Hons) Mechanical Engineering from MITS, Gwalior. Having flair for endless learning, has done more than 50+ certifications and specializations online on IoT, Augmented Reality, Gamification, Machine Learning from University of California at Irvine, Wharton School, University of Pennsylvania, Deep \href{http://Learning.AI}{Learning.AI} and Google Machine Learning Group. His research interests are in the areas of Robotics, Mechatronics, Bioinformatics, Internet of Things (IoT), AI \& ML using Tensor Flow (CMLE) and Gamification. In 2001 he was appointed as Assistant Controller (Technology), Ministry of IT, Govt. of India by the Honourable President of India in the Office of the Controller of Certifying Authorities (CCA). In 2013 he was accorded the role in the National Advisory Board for What Can I Give Mission- Kalam Foundation of Dr. APJ Abdul Kalam. In 2011 he received the MIT Technology Review Grand Challenge Award followed by the coveted Infosys InfyMakers Award in the year 2016. Recently collaborating in research project with Stanford University. Has been nominated as the Chairman to ASSOCHAM North Region Council for Innovation. Sports enthusiast having flair to amalgamate data points from sports with algorithms for performance assessment and predictive analytics. Has authored 7 books on IoT, Mobile Robotic Platforms, Biomedical Sensors and Machine Learning \& Data Analytics. $150+$ patents, $100+$ published, 10 Granted.

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