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Journal of Manufacturing Processes 75 (2022) 415–434
Available online 19 January 2022
1526-6125/© 2022 The Authors. Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers. This is an open access article under the CC
BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Review 
Process parameter selection and optimization of laser powder bed fusion for 
316L stainless steel: A review 
N. Ahmed a, I. Barsoum a,b,c,*, G. Haidemenopoulos d, R.K. Abu Al-Rub a,b 
a Advanced Digital & Additive Manufacturing Center, Khalifa University, Abu Dhabi, United Arab Emirates 
b Department of Mechanical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates 
c Department of Engineering Mechanics, Royal Institute of Technology (KTH), Stockholm, Sweden 
d Department of Mechanical Engineering, University of Thessaly, Volos, Greece 
A R T I C L E I N F O 
Keywords: 
Selective Laser Melting 
Process parameter optimization 
SS316L steel 
Densification 
Microstructure 
A B S T R A C T 
Stainless steel 316L has been an extensively investigated metallic material for laser powder bed fusion (L-PBF) in 
the past few decades due to its high corrosion resistance. However, there are challenges related to producing L- 
PBF parts with minimal defects, attaining mechanical properties comparable with traditional process and de-
pendency on time consuming post process treatments. The selection of L-PBF process parameters is crucial to 
overcome these challenges. This paper reviews the research carried out on L-PBF process parameter optimization 
for fabrication of 316L steel components for maximizing part densifications and attaining desired microstructure 
morphologies in parts. A brief work on numerical simulation approach for process parameter optimization for 
high densifications is also included in this paper. 
1. Introduction 
The laser powder bed fusion (L-PBF), also known as selective laser 
melting (SLM) involves the interaction between the laser heat source 
and metal powder particles occurring in a layer-by-layer pattern to form 
a solid part. Although the terms L-PBF and SLM are interchangeably 
used for the laser process [1–5], we adopt the term L-PBF for our dis-
cussions throughout this review. Even though L-PBF seems simple in 
principle, it involves a highly complex multiphysics phenomenon 
occurring in the powder bed, in resulting molten pool and in the solid-
ifying phase of the parts. A clear understanding of influence of the 
various 3D printing parameters on the outcome of the process is essen-
tial to build a good quality part. As illustrated in Fig. 1 the multitudes of 
parameters available in the L-PBF process are known to influence the 
material's structure, and the structure in turn influences the final prop-
erties of built part. Hence, these process-structure-property (PSP) re-
lationships must be clearly understood to optimize the process for a 
defect free part. The major defects that are typically observed in L-PBF 
parts are porosity and microstructural defects [6–10]. 
A poorly chosen parameter combination may result in insufficient 
powder fusion, keyhole formations and balling phenomenon. An illus-
tration in Fig. 2 shows the choice of laser power and scanning speed 
affecting the outcome of process. A high laser speed and insufficient 
laser power results in insufficient melting of powder leading to lacks-of- 
fusion porosity. On the contrary, a choice of too high power for a given 
speed results in overheating, causing deeper laser penetration, leading 
to pore formations of inert gas know as keyholes [10]. The deeper 
penetration due to excessive laser power will also affect the micro-
structures of already solidified layers. Fig. 2 suggests that we can in-
crease both the power and speed within the range of safe operating 
window for minimum defects. However, increasing the power and speed 
beyond a certain limit will also lead to an unstable melt pool behavior 
and formation of small spherical balls known as balling phenomenon 
which leads to discontinuous melt tracks. 
Hence, it is imperative to find the optimum process parameters 
window prior to fabrication; however, due to the vast number of many 
parameters (more than 100 [10]) considered to affect the deposition 
process, the task of understanding and optimizing all the parameters is 
extremely challenging. Numerous studies have identified critical pa-
rameters such as laser power, scanning speed, hatching spacing, layer 
thickness and scanning strategies having major influence on properties 
of the final part. 
Review article on Ti64Al by Shipley et al. [11] focus on optimizing 
process parameters to achieve high densifications and microstructure 
morphologies in L-PBF samples. High densifications up to 99.9% is re-
ported and the microstructure of as-built Ti64 is observed to be 
* Corresponding author at: Advanced Digital & Additive Manufacturing Center, Khalifa University, Abu Dhabi, United Arab Emirates. 
E-mail address: imad.barsoum@ku.ac.ae (I. Barsoum). 
Contents lists available at ScienceDirect 
Journal of Manufacturing Processes 
journal homepage: www.elsevier.com/locate/manpro 
https://doi.org/10.1016/j.jmapro.2021.12.064 
Received 29 September 2020; Received in revised form 14 October 2021; Accepted 25 December 2021 
mailto:imad.barsoum@ku.ac.ae
www.sciencedirect.com/science/journal/15266125
https://www.elsevier.com/locate/manpro
https://doi.org/10.1016/j.jmapro.2021.12.064
https://doi.org/10.1016/j.jmapro.2021.12.064
https://doi.org/10.1016/j.jmapro.2021.12.064
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jmapro.2021.12.064&domain=pdf
http://creativecommons.org/licenses/by-nc-nd/4.0/
Journal of Manufacturing Processes 75 (2022) 415–434
416
martensitic having high strength but low ductility (<9%). Various 
studies on parameter optimizations focus on transforming α’ martensite 
into a α + β structure to improve its mechanical properties. Also, review 
by Zhang et al. [12] on Al alloys by L-PBF highlighted the difficulties in 
laser formability of Al alloys comparison to Ti and Fe alloys as Al alloys 
have high laser reflectivity (~91%) and high thermal conductivity (237 
W/mK) which results in high cooling rate leading to cracking. However, 
these reviews do not consider the numerical modeling approaches which 
play an important role in L-PBF parameter optimizations studies. The 
316L stainless steel is widely used in industries and is the most feasible 
material for L-PBF process due its high weldability compared to Ti and 
Al alloys. A comprehensive review on L-PBF parameter optimization 
covering both the experimental and numerical work specific to SS316L 
material is rare to be found. Hence, this paper provides a review of the 
literature with the objective to attain high densifications, high me-
chanical properties (strength and ductility) and microstructural mor-
phologies in SS316L material. A brief work carried out by the authors on 
simulation approach for investigating L-PBF process parameters to 
attain high densification in SS316L steel specimens is also included in 
the numerical modeling section. 
2. Densification 
2.1. Effect of process parameters 
Stainless steel 316L (SS316L) is one of the materials where there has 
been significant success in achieving near full density using L-PBF pro-
cess [9]. This section reviews the work done on primary process 
parameter optimization along with other strategies to attain high den-
sifications specific to SS316L specimens. Majority of the work 
[7,8,13–15] on L-PBF parameter studies utilizes the influence of laser 
energy density on part densification. As given by Eq. (1), the energy 
density (E) is related to the primary process parameters such as laser 
power (P), scanning speed (v), hatch spacing (h) and layer thickness (t) 
[7], the parameters of interest are illustrated by the author in Fig. 3. The 
laser power (P) is the total energy emitted by the laser in unit time,the 
scanning speed (v) is velocity at which the laser spot moves across the 
powder bed along the scan vectors, hatch spacing (h) is the distance 
between adjacent scan vectors and layer thickness (t) is the depth of 
powder layer melted on the powder bed. 
E =
P
v h t
(1) 
A comprehensive investigation on interaction of the process pa-
rameters and its effect on densification is imperative to produce high 
quality parts. One such work carrying out full factorial permutation 
involving laser power, scanning speed, and hatch spacing was done by 
Yakout et al. [8]. Using the influence of energy density on resulting 
densification in L-PBF, Yakout successfully established range of energy 
densities illustrated in Fig. 4 (a) which results in stable melting, 
continuous beads, and uniform melt tracks. Lower values of laser energy 
density showed significant porosity due to formation of voids and lack of 
powder fusion, and the higher energy density values beyond a certain 
limit resulted in decreasing densification leading to unstable melt pool, 
vaporization, and micro-segregation of alloying elements. This can also 
be well understood from the observations made from the fractography of 
SS316L specimens built with various laser energy density values as 
shown in Fig. 4 (b), (i) Brittle fracture observed in specimen build with 
lower energy density value of 41.7 J/mm3 due large number of voids 
present (ii) Brittle-ductile failure observed in specimen built with energy 
density value of 62.5 J/mm3 shows lesser voids present in comparison 
with specimen built with lower value (iii) Ductile failure observed in 
specimen built with higher energy density value of 156.3 J/mm3 shows 
mass vaporization of the metal. Hence, the determination of appropriate 
energy density range becomes vital for achieving parts with minimum 
porosity defects. 
Fig. 5 illustrates the laser energy density ranges collected from 
various literature and their resulting densifications in L-PBF processed 
SS316L specimens. The different combinations of laser parameters such 
as laser power, scanning speed, hatch spacing and layer thickness have 
been used arriving at optimizing laser energy density. For higher 
densification (above 99%), the optimum range of energy density values 
are scattered in the range between 50 J/mm3 to 150 J/mm3. Also, it is 
observed that even for the same energy density values for example for 
100 J/mm3 the resulting densification values are different indicating 
Fig. 1. Relationship between process parameters, structure and properties in L-PBF process. 
Fig. 2. Influence of laser power and scanning velocity on the build outcome. 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
417
Fig. 3. Illustration of process parameters for study in L-PBF process. 
(a)
(i) E = 41.7 J/mm3
Void formation
Brittle fracture
(ii) E = 62.5 J/mm3
Stable melting
Brittle-ductile fracture
(iii) E = 156.3 J/mm3
Vaporization
Ductile fracture
(b)
Fig. 4. (a) Effect of various laser energy density values on resulting densification (data adapted from [8]), (b) Fractography of SS 316L samples produced at laser 
energy density value of (i) Lower value of 41.7 J/mm3 results in “brittle fracture” due to presence of voids (ii) 62.5 J/mm3 results in “brittle-ductile” fracture due to 
less voids present (iii) 156.3 J/mm3 results in “ductile” failure due to vaporization of powder [8]. 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
418
dependency on the individual values of laser power, scan speed, hatch 
spacing and layer thickness used. For instance, in the work reported by 
Tucho et al. [7], for a two parameter set with same energy density value 
of 80 J/mm3, the parameter set having lower hatch spacing of 0.08 mm 
showed higher densifications of 99.7% compared with parameter set 
with higher hatch spacing of 0.14 mm with densification of 99.27%. This 
is primarily owing to tighter overlap of melt pools for bonding provided 
by smaller hatch spacing which reduces porosity. Tucho in his experi-
ments also varied the laser power (150 W to 200 W), scan speed (400 
mm/s to 1600 mm/s) and hatch spacing (0.08 mm to 0.14 mm) with 
constant layer thickness of 0.03 mm to study the influence of laser en-
ergy density in the range of 50–80 J/mm3. A comparison of SEM images 
of the specimen for the energy density range is shown in Fig. 6, samples 
built with lower energy density value of 50 J/mm3 results in porosity 
due to lacks-of-fusion while the sample built with higher energy density 
value of 80 J/mm3 results in nearly defect free parts. Lower hatch 
spacing (0.08 mm) and relatively high scan speeds (800 mm/s) is sug-
gested as a good combination to attain density above 99% for SS316L 
specimens. 
Cherry et al. [13] also showed similar trends about increase in energy 
density leading to reduction in porosity using a modulated laser system. 
The exposure time and overlap distance is used to vary the energy 
density and arrive at an optimum limit value of 105 J/mm3 for achieving 
densification up to 99.6%. Cherry also observed that there is a limit up to 
which energy density may be increased as values above 105 J/mm3 
showed signs of balling effect, and increase of pores size was observed 
due to vaporization of low melting elements. Liverani et al. [14] also 
showed that an increase in energy density up to a value of 100 J/mm3 
increased densification (99.9%) using a combination of laser power in 
the range of 90–150 W and scan speed between 500 and 900 mm/s. The 
study highlighted the fact that of all the parameters laser power was the 
dominant factor in the energy density that contributed to higher 
densification of L-PBF samples. Zhong et al. [17] in his work on light 
weight lattice struts using SS316L material observed that the optimum 
energy density is in the similar range up to 110 J/mm3 for higher den-
sifications of parts. AlMangour et al. [18] showed influence of laser 
scanning speed on densification by using lower scan speed of 50 mm/s 
achieved maximum densification up to 98.22%. Lower scan speeds 
result in heat accumulation required for fully melting the powder and 
forming dense structures. 
L-PBF process parameters such as laser power, scanning speed, hatch 
spacing and layer thickness have their effects individually and may be 
also optimally combined to attain a higher densification. An optimum 
combination using higher laser power, lower scan speed, narrower hatch 
spacing and thinner layer thickness enables higher densification and 
avoid balling as suggested by Li et al. [19]. It was also observed that use 
of gas atomized powder gave better results compared to water atomized 
powder due to lower oxygen content and smaller mean particle size 
which assists densification. The combined effect of scanning speed and 
layer thickness on densification was demonstrated by Kruth et al. [20]. 
Fig. 5. Ranges of energy densities and corresponding densification values collected from various literature for SS316L specimens; Tucho [7], Kamath [10], Cherry 
[13], Liverani [14], Larimian [16]. 
Fig. 6. SEM images of higher porosity defects due to lacks-of-fusion observed using energy density value of 50 J/mm3 (left) compared with minimal defects with 
higher energy density value of 80 J/mm3 (right) [7]. 
N. Ahmed et al.Journal of Manufacturing Processes 75 (2022) 415–434
419
As shown in Fig. 7 the relative densities of samples is almost indepen-
dent of various layer thickness values (20 μm, 30 μm, 40 μm) at suffi-
ciently low scan speeds. However, at high-speed values above 350 mm/s 
resulted in lower densification and is more severe for higher layer 
thickness value of 40 μm due to lack of enough laser depth penetration 
and poor bonding of layers due to lower laser exposure duration. 
A statistical approach using ANOVA (analysis of variance) method 
along with experimentation was used by Miranda et al. [9] to study the 
influence of single parameter as well as multiple parameter interactions 
on densification. In single parameter study it was observed that 
increasing laser power results in high densification, whereas for laser 
speed and hatch spacing, increasing values resulted in lower densifica-
tion due to poor powder melting. In multiple parameter studies, it is seen 
that high density can be achieved by combining intermediate values of 
laser power and hatch spacing as shown in Fig. 8 (a), and also by 
combining intermediate values of laser power and scan speed as shown 
in Fig. 8 (b). 
Although L-PBF is known to be a typically a slow additive 
manufacturing process and to increase the build rate by increasing pa-
rameters such as scan speed, hatch spacing and layer thickness has its 
limitation and affects the part quality. However, there has been attempts 
to attain high densification along with high build rates. In recent work 
by Liu et al. [6] used 200 μm layer thickness and increased the build rate 
to 12.4 mm3/s (2.5–10 times higher than using lower layer thickness) 
achieving a densification of 99.99% in SS316L samples. It was observed 
that for increased layer thickness, laser exposure time (120–160 μs) 
along with laser overlap (40%) plays a key role in removing defects such 
as unmelted powder and balling. Sun et al. [15] used higher range of 
power and speed compared to commonly used ranges reported to in-
crease the overall build rate by ~72%. Sun et al. in his work built 10 mm 
side cubes using power of 380 W and speed up to 2000 mm/s to achieve 
high densification rates of 99.9%. Kamath et al. [10] used high laser 
power up to 400 W and found a wide range of high speeds (1900 mm/s 
to 2200 mm/s) achieving 99.5% densification. An increase in layer 
thickness to reduce build time was demonstrated by Shi et al. [21], using 
250 μm layer thickness and laser power of 400 W was able to achieve 
densification above 99% using with various combinations of scan speed 
and hatch spacing. Hence, careful selection of parameters makes it 
possible to attain good part quality along with high build rates. 
The quality of melt pool formed during laser melting process is an 
indicator of laser power and speed used during the process. The 
influence of melt pool dimension on densification was experimentally 
shown by Wei et al. [22], optimum combination of laser power and 
speed was used to give a stable and continuous melt pool that resulted in 
higher densifications. Wei et al. also observed that lower layer thickness 
(~100 μm) resulted in higher density compared to higher layer thick-
ness values for same process parameters (98 W power, 90 mm/s speed 
and 70 μm hatch spacing) owing to complete powder melting and suf-
ficient bonding between layers. 
2.2. Scanning strategies 
As discussed in preceding section, L-PBF parameters such as laser 
power, scan speed and hatch spacing have been widely used for influ-
encing density. However, the scanning strategies along with process 
parameters also play an important role in higher densification. Appro-
priate hatch spacing and laser scanning patterns ensure enough heat 
accumulation required for bonding between tracks as seen in work by 
Wang et al. [23] using a strategy called inter-layer staggered scanning 
strategy illustrated in Fig. 9 (a) to achieve densification of more than 
98% in SS316L samples. In this strategy, the first two adjacent layers are 
scanned in staggered pattern, and the next two adjacent layers are 
scanned orthogonally to the previous two layers. This scanning pattern 
removes defects formed in previous layer. Salman et al. [24] experi-
mented with different laser scanning patterns for a fixed process 
parameter set of laser power (175 W), scanning speed (668 mm/s), 
hatch spacing (120 μm) and layer thickness (30 μm). Densification up to 
99.9% was achieved using stripe and contour strategy shown in Fig. 9 
(b), in this scan paths are parallel to one other in unidirectional fashion 
and also includes a contour scan around the perimeter of the build for 
finishing purpose, such a scan pattern ensures good overlap between the 
scanned tracks and leads to almost fully dense samples. 
Various scanning strategies was studied by Larimian et al. [16] for 
316L stainless steel samples illustrated in Fig. 9 (c) as type A, B and C. 
The samples processed using scanning strategy type - A achieved higher 
densification (99.45%) as compared to samples processed with scanning 
strategy type - B (97.7%) and type - C (98%). The scan strategy type- A 
resulted in higher cooling rates thus leading to a refined structure and 
high densification. Similarly, there are other scanning patterns used to 
attain high quality parts for instance the island strategy shown in Fig. 9 
(d), in this strategy the build layers are divided into independent and 
randomly scanned sub regions [25]. Scan vectors of surrounding islands 
Fig. 7. Influence of scanning speed on densification for different layer thicknesses (adapted from [20]). 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
420
are scanned perpendicular to one another thus creating each layer with 
tracks scanned in multiple directions. This checkered strategy resulted in 
densification of 99% due to even melting of powder in all directions of 
the melted powder layer. 
2.3. Preheating and remelting strategies 
The approach of preheating powder bed to attain relative high 
densities above 99% was demonstrated by Zhang et al. [26]. From 
micrograph comparison of samples in Fig. 10 for different values of 
preheating, pore formations are observed along the scanning direction 
in sample without preheating and gradually become minimal in samples 
built with increasing preheating temperatures of powder bed. A pre-
heating temperature range of 150–200 ◦C resulted in a densification of 
99.4% - 99.7% and a homogenous structure. Preheating improves the 
heat absorptivity of the powder and improves the wettability properties 
of substrate resulting in complete melting of powder [27]. 
Other approaches like remelting layers as a means of achieving 
higher densification has been employed by Yasa et al. [28]. A density of 
99.8% was achieved in samples by applying one scan of remelting 
compare to density of 99.2% in samples without remelting. Remelting 
layers eliminates any remaining porosity in between neighboring melt 
Fig. 8. (a) Combination of power and scan spacing, (b) combination of power and scan speed used for achieving high densification [9]. 
Fig. 9. Illustration of different laser scanning strategies used for attaining densification (a) Inter layer staggered strategy (b) Stripe and contour strategy (c) Different 
laser scanning strategies [16] (d) Checkered island strategy. 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
421
pools, and the strategy may be employed for parts in which density maybe critical for crack formation and propagations. Using surface remelting 
strategies, Ghorbani et al. [29] demonstrated the possibility to achieve 
surface roughness of less than 1 μm for 316L specimens built at an 
inclination angle of 45◦. A combination of four parameters; remelting 
shell layer thickness (150 μm), laser point distance (50 μm), laser 
exposure time (400 μs) and laser power (150 W) were used to attain a 
high surface quality where the laser exposure time being the most 
influencing parameter. It was also observed that values of optimized 
parameters for remelting the surface after the part is built are different 
from values that are used to actually build the part, and that hence 
remelting parameters should be individually optimized. Qiu et al. [30] 
in their work on 316L specimens observed that laser power was the 
dominating parameter influencing the porosity and demonstrated the 
removal of open pores at the outer surface using remelting. The pores 
were almost absent at interface of the uppermost layer and its immediate 
layer below. However, in contrast all the other subsequent layers below 
showed presence of interlayer pores due to entrapping of gas bubbles. 
3. Strength and ductility 
SS316L steel parts produced via L-PBF are comparable and some-
times superior mechanical properties to those of bulk materials. Selec-
tion of the appropriate process parameters plays a very important role in 
outcome of final mechanical properties. This section reviews few 
research done to study the effect of L-PBF primary process parameters on 
the mechanical properties of the built parts. Lu et al. [31] their work 
used remelting strategies for individual layers to improve the tensile 
strength from 674 MPa to 725 MPa and Yield strength increased from 
591 MPa to 643 MPa compared to SS316L specimens with no remelting. 
Remelting process removes the defects such as unmelted powder and 
balling, and improves the bonding between the melt pools. Liverani 
et al. [14] in his work on mechanical characterization of SS316L steel 
samples varied laser power with build orientation of 45◦and 90◦ to the 
base plate to study its effects on tensile properties. The samples were 
also compared with specimen built from traditional annealing process. 
As shown in Fig. 11(a) it is observed that laser power influences elon-
gation to failure (E %) and had very little effect on yield stress (YS) and 
ultimate tensile strength (UTS). A decrease in laser power from 150 W to 
Fig. 10. Effect of preheating temperature on densification of SS316L [26]. 
Fig. 11. (a) Influence of laser power and orientation on mechanical properties of SS316L specimens, and comparison with the reference material [14], (b) engi-
neering stress-strain curves obtained from tensile test of L-PBF processed 316L stainless steel samples for different scanning speeds (data referred from [16]). 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
422
100 W caused reduction of about 10% in the elongation to failure for 
both build orientations (45◦and 90◦). The reason is attributed to the 
lower density of samples, formation of voids and cavities at lower power 
of 100 W. Also, it was observed that in build orientation angle of 45◦
there was increase in yield strength to about 10–20% and UTS to about 
12–13% while the elongation decreased by 50% in comparison with 
samples built at orientation angle of 90◦. The increase in strength is 
attributed to the temperature gradient which decreases with increase in 
orientation angle, the associated crystallographic orientations and 
ensuing anisotropy which is explained by Du et al. [32] in his studies on 
influence of build orientation on L-PBF of IN718. 
Larimian et al. [16] in his research studied the effect of different 
scanning speeds, scanning strategies and energy densities on the tensile 
strength of the 316L steel. It was found that for samples which were 
processed at higher scanning speed of 0.25 mm/s exhibited higher yield 
strength, ultimate tensile strength, and elongation as compared to 
samples processed at lower scanning speed of 0.239 mm/s. One such 
comparison of tensile test of steel specimen built using scan strategy ‘A' 
for the two speeds is shown in Fig. 11(b). The reason for better me-
chanical properties is attributed to the refined microstructure that result 
from high cooling rate attained at higher scan speeds. Mower et al. [33] 
in his work on mechanical behavior of SS316L specimens observed high 
ductility with elongations of approximately 30% at failure. Also, the 
yield stress in samples built horizontally (496 MPa) and at a 45-degree 
inclined (473 MPa) was much higher compared to the yield stress of 
annealed 316L specimen (345 MPa). The higher yield stress is attributed 
to the fine crystalline structure created by the rapid cooling during the 
process. 
A summary of maximum values of yield stress, tensile strength and 
elongation of SS316L specimens from L-PBF reported in various litera-
tures is represented in Fig. 13. It can be observed that there is large 
variation in values for attained properties values from different ranges of 
energy density of the laser process. The reason for variation of property 
values across literatures is attributed to the built-in defects inherited 
during L-PBF process and also to the shape effects of various build ge-
ometries as inferred by Li et al. [34] on their work on SS316L specimens. 
Li et al. [34] produced two tensile specimens from L-PBF process, one 
directly printed specimen (red circle in the Fig. 12) and other specimen 
machined out from as-built rectangular plate referred to as plate samples 
(blue triangle) from L-PBF by varying the laser power (150 W–350 W), 
speed (700 mm/s–1700 mm/s). Li achieved tensile strength higher 
compared to those reported in various literatures and attributed superior 
mechanical properties to the near full densified samples (near or above 
99%) achieved in the process. Also, the directly printed tensile and plate 
samples showed different tensile and elongation values thus ascertain-
ing the fact that the build geometries influence the final part properties. 
Although there is a usual tradeoff between tensile strength and 
ductility in both L-PBF and in the traditional counterparts, a way of 
simultaneously attaining higher values of both properties has been 
demonstrated by few researchers. Wang et al. [44] in his work used 
optimized parameters to attain density above 99.2% in samples and high 
yield strengths (590 MPa) which is two to three times stronger than 
counterpart grades of as-cast (160 MPa) and as-wrought (365 MPa), the 
tensile elongation was up to 60% which surpass even the traditional 
high performance steel (~ 35%) with similar strengths. Wang attributed 
the superior values of strength to solidification- enabled cellular struc-
tures, low-angle grain boundaries, and presence of dislocations, while 
high uniform elongation was attributed to the steady and progressive 
work-hardening controlled by a hierarchically heterogeneous micro-
structure. Similarly, Sun et al. [45] demonstrated that higher laser 
power of 950 W and multi-scan scanning strategy can be used to tailor 
the crystallographic structure and grain size to increase both strength 
and ductility at the same time. A comparison with samples processed 
with lower power of 400 W showed an increase in the true ultimate 
tensile strength up to ~16% and true strain value up to ~40%. Thus, the 
optimized parameter selections may be used in L-PBF process may be 
used to overcome the limitation of traditional strength and ductility 
tradeoff. 
4. Discussion - parameter optimization 
As obvious from the literature abound, most of the work focuses on 
laserenergy density as a design parameter to achieve high densifications 
in 316L specimens. Various combinations of process parameters such as 
laser power, scanning speed, hatch spacing, layer thickness, scanning 
strategies have been used to establish the appropriate energy density 
range for higher densifications, of this, laser power, scanning speed and 
hatch spacing are the major adjustable parameters for a L-PBF machines, 
with the powder particle size and layer thickness usually fixed for L-PBF 
machines. Most of the research focuses on one machine and its 
Fig. 12. The comparison of yield strength and strain values of SS316L specimens done by Li et al. [34] with values reported from various literature. 
N. Ahmed et al. 
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parameter set, while part variability across multiple printers due the 
stochastic nature of AM process remains largely unexplored. However, 
there are studies done on polymers with multiple but identical machines 
to investigate part variability by McGregor et al. [50], a framework was 
developed based on experimental and statistical methods investigating 
how the multiple machines, tools and process parameters impact the 
dimensional accuracy of the parts. Such detailed studies with multiple 
machines for metallic alloys like SS316L are rare to find. Table 1 sum-
marizes the optimized process parameters set collected from various 
literature for different commercial L-PBF machines along with corre-
sponding laser energy density and highest densification reported in 
samples. It can be observed that energy density for achieving higher 
densification is spread over wide range of values (53 J/mm3–238 J/ 
mm3) indicating that the optimized process parameters set is specific to 
the individual machines, and depends on the flexibility the machine 
offers in terms of modifying the parameters, and hence the values cannot 
be entirely generalized. 
Although, we observed that the use of energy density has been used 
as a criteria for optimizing L-PBF process, it has to be dealt with caution 
as observed by Bertoli et al. [49] in his experiments, for the same values 
of energy density value but with different parameter combinations 
yielded different densification values. One such trail of experiment by 
varying the laser power and scan speed done by Bertoli et al. is shown in 
Fig. 14. Using laser energy density value of 242 J/mm3, the higher 
ranges of laser power and scan speed resulted in irregular track forma-
tion with series of barely connected metal beads, while only one com-
bination of lower power (100 W) and lower speed (100 mm/s) attained a 
continues smooth track. Hence, there exists a very narrow band of laser 
energy density that can applied and that laser energy density by itself is 
not enough to fully encompass the complex physical phenomenon of L- 
PBF process. 
5. Microstructure morphologies 
The microstructure (e.g., grain geometry, dislocation cells, pre-
cipitates, etc.) of the melt pool formed during the solidification process 
has a strong influence on final mechanical properties of the L-PBF-pro-
cessed parts. The microstructures formed during the process is known to 
be directly influenced by the cooling rates during the solidification 
process [51–53]. The cooling rate is a result of combination of L-PBF 
process parameters and can be used to control the microstructure and 
thereby enhance the mechanical properties of build. This sections 
Fig. 13. Comparison of yield stress, tensile strength and elongation collected from various literatures: Liverani et al. [14], Larimian et al. [16], Ahmadi et al. [36], 
Suryawanshi et al. [37], Mertens et al. [38], Yadollahi et al. [39], Liu et al. [40], Yadroitsev et al. [41], Zheng et al. [42], Zhong et al. [43]. 
Fig. 14. Five tracks deposited with the same energy density value = 242 J/mm3 but with increasing laser power and scan speed going from top to bottom [49]. 
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Fig. 15. (a) For lower laser power (400 W) the over lapping melt pools are half cylindrical in shape (b) For high power(1 kW), the melt pools are much wider and 
shallower (c) Lower power resulting in small cell spacing compared to high power in (d) [54]. 
Fig. 16. a) Overlapping melt pools ensure sufficient bonding of layers b) columnar grains indicated by white arrows cross the fusion line shown by open arrow, black 
arrows indicated the build direction [7]. 
Fig. 17. Comparison of microstructure a) without layer remelting and b) with layer remelting [28]. 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
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reviews work done on influence of process parameters on microstruc-
tural evolution, grain growth, and phases in 316L steel specimen from L- 
PBF process. 
Studies have been conducted to understand the effect of cooling rates 
resulting from process parameter on the final microstructure. Larimian 
et al. [16] exclusively studied the effect of various scanning strategies 
and scanning speeds on microstructure of 316L cube samples. It was 
observed that scanning strategy which resulted in high cooling rates 
gave rise to a refined microstructure compared with scanning strategies 
with low cooling rates, also, high scanning speeds produces higher 
cooling rates which shortens the grain growth rate resulting in refined 
microstructure compared with those processed at lower speeds. Simi-
larly, Salman et al. [24] observed that different scanning strategies 
resulted in different cell and grain sizes and reported only austenite 
phase in the final parts. The work demonstrated that grain size largely 
influenced the tensile loading capacity of the specimens and can be 
tailored by adjusting process parameters. Work by Liverani et al. [14] 
investigated the effect of varying fabrication orientation angle and 
found the presence of columnar grains in the direction of respective 
build orientation. The work also confirmed the formation of only FCC 
austenitic phase. Montero-Sistiaga et al. [54] experimented with high 
and lower power lasers with different scan rotation angles to study its 
effects on resulting 316L microstructure. A low power laser of 400 W 
resulted in smaller well overlapped melt pool shown Fig. 15(a) 
compared to the wide and shallow melt pool in laser power of 1 kw 
shown in Fig. 15(b), the lower laser power resulted in smaller columnar 
grains shown in Fig. 15(c) whereas comparatively high power of 1 kW 
resulted in long columnar grains in the build direction as shown in 
Fig. 15(d). 
Tucho et al. [7] studied the resulting microstructure of 316L steel for 
different energy densities. For the optimum energy density range, the 
optical images in Fig. 16(a) show well overlapped melt pools that 
indicate successful powder fusion and layer bonding. The work indi-
cated columnar grains cross and grow beyond the fusion lines seen in 
Fig. 16(b). The grain growth is predominantly perpendicular to the melt 
pool boundary and in the thermal gradient direction. 
Wang et al. [55] emphasized the need for selection of a process 
parameter that results in refined cellular dendrites which leads to high 
mechanical strength. The influence of energy density by varying scan 
speeds on dendrite spacing was studiedand optimal values above 125 J/ 
mm3 which resulted in small dendrite spacing and no residual pores was 
determined. The small cell spacing is due to the high cooling rate 
associated with high scanning speeds that shortens the time available for 
dendrite coarsening. The hardness and tensile properties of the parts 
were found to be superior compared to traditional wrought SS316L. 
Zhong et.al [43] in his study also found increases in strength of L-PBF 
parts compared with traditional counterparts, the reason is attributed to 
the combined effect of intragranular cellular segregation network 
structure and by the pinning effect of dislocations due to the small cell 
spacing which resists deformations. The decrease in cell spacing with 
increase in range of laser scanning speed was also confirmed by 
Yadroitsev et.al [56], suggesting scanning speed is more suitable 
parameter for controlling microstructure evolution in L-PBF process. 
Other strategies such as remelting of previous formed layer to attain 
homogenous microstructure have been used by Kruth et al. [20] and 
Yasa et al. [28]. A comparison shown in Fig. 17 indicates the remelting 
of layers leads to a significantly finer lamellar microstructure without 
any pores compared to microstructure without layer remelting. 
Numerical modeling has also been used to reduce the experimental 
effort to investigate the microstructures. The numerical models predict 
the melt pool characteristics and thermal behaviors during the process 
and relate the influence of L-PBF parameters on microstructure. 
Fig. 18. Microstructure of individual layers for different samples (a) single layer, (b) triple layer; (c) uinform honeycomb microstructure structure observed in the 
single-layer; (d), (e) and (f) shows the tabular microstructure and variation in grain size due to thermal cycling for first layer, second layer and third layer respectively 
(referred from Liu et al. [57]). 
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Fig. 19. (a) Finite element model to predict melt pool dimensions for single track deposition. (b) Comparison of deposited layer thickness of the built(left) with 
predicted melt pool depth(right) [63]. 
Fig. 20. a) Porosity prediction based on melt pool size (b) Stress-Strain curves based on number of melt pool [66]. 
Fig. 21. The melt pool dimensions; median length (l), median width (w) and median depth (d) considered for evaluating appropriate laser power/speed combi-
nations (referred from [68]). 
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Aggarwal et al. [58] in their work studied the effect of different melting 
modes (conduction and keyhole) on the microstructure of 316L parts 
fabricated from L-PBF. The different melting modes are attained by 
altering the laser intensity and the mesoscale model developed predicted 
accurately the melt pool geometries considering the spatial arrange-
ments and phase transition of powder particles. The experimental 
investigation of the samples revealed that the keyhole mode resulted in 
finer cellular grains with mean cellular spacing decreasing from about 
0.45 μm to 0.28 μm compared to that with conduction mode, the 
refinement of grains is attributed to the remelting of the previously 
deposited layers and increased cooling rates due to the surrounding 
solidified bulk material. Similarly, Liu et al. [57] in their study used FE 
models to study the influence of subsequent thermal cycling on the 
microstructure using a single and a three layer numerical model. The 
numerical model successfully predicted the melting track dimensions 
and thermal behaviors for the L-PBF samples. The microstructure 
investigation shown in Fig. 18 on the individual layers revealed that the 
grain size in single layer sample was finer and uniform compared to the 
three-layer sample due to the faster heat dissipation to the solid sub-
strate. However, in the three-layer sample there was variation in 
microstructure along the height of the build with a smaller grain size 
found in the topmost (third layer) compared to the finer grains in second 
and first layers below. The first and second layer undergoes reheating 
and hence subjected to greater thermal gradients compared with third 
layer. Although, the third layer does not undergo subsequent thermal 
cycling the grains size is much larger compared to the single layer 
sample due to elongated heat flow paths. From these observations, we 
can also infer that the laser intensity along the build height may be 
Fig. 22. Various combination of laser power and scanning speed considered for study; green squares indicating combination giving high densification (> 99.9%) 
(refer values from Table 3. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 
Fig. 23. Effect of varying hatch spacing on densification for different laser power and scanning speed. 
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varied in order to attain thermal gradients that may result in uniform 
microstructures. 
5.1. Discussion – microstructure morphologies 
In the literature reviewed it is observed that formation of columnar 
grain is typical for the 316L L-PBF process due to fast solidifications in 
locally melted areas. The grains grow in the direction of the thermal 
gradient which is predominantly the build direction. Combination of 
process parameters such as laser power, scan speed, hatch spacing and 
scanning strategies have been used that result in high cooling rates to 
attain refined microstructures in L-PBF process, hence it is possible to 
achieve superior mechanical strength without sacrificing the ductility 
compared with traditional methods as mentioned in Section 3, 
6. Modeling for parameter optimization 
6.1. Numerical modeling for high densification 
As observed in the preceding sections, optimization of process 
parameter involves lengthy experimental trails with different ranges of 
process parameters [1–30]. The mechanical properties of the printed 
specimens must be then experimentally evaluated to understand the 
influence of process parameters, although an accurate approach, its 
time-consuming and expensive to carry out many investigative itera-
tions. In contrast, numerical modeling approaches provide a faster and 
economical way to effectively investigate the influence of different 
process parameters values on part properties. The basics of modeling 
approach for L-PBF process, the relevant theoretical background, 
different heat source modeling technique, use of temperature dependent 
material properties, relevant heat equations etc. can be found in various 
literatures [59–64]. Finite element models have been extensively used to 
predict the dimensions of melt pool for corresponding input process 
parameters. An illustration by the author in Fig. 19 (a) shows a finite 
element model for a single-track deposition to predict the melt pool 
dimension as the laser traverses the path. The melt pool depth, width 
and length formed during process is representative of the input pa-
rameters and can indicate whether suitable conditions have been met to 
attain desired part quality [65]. 
The effect of varying primary parameterslike laser power and speed 
on melt pool dimensions in SS316L material have been investigated in 
studies using finite element method [61,63]. The effect of scanning 
speed on melt pool dimensions and behavior of melt pool using the finite 
element was studied by Foroozmehr et al. [63], the simulated melt pool 
depth compared well with the measured thickness of the deposited layer 
of samples. A case comparison for a speed of 80 mm/s is shown in Fig. 19 
(b), the predicted melt pool depth is 178.5 μm in comparison with actual 
deposited layer thickness of 200.4 μm with a 10.8% error. It was 
observed that melt pool stabilized after only a few initial scan tracks 
indicating simulating initial 3–4 tracks was enough to understand melt 
pool behavior, hence saving simulation time. Similar validation was 
done by Hussein et al. [61] comparing predicted melt pool depth with 
layer thickness of the built specimen, it was observed that increase of 
scan speed resulted in elongated length of the melt pool while the width 
and depth of the melt pool decreased. 
More advanced numerical methods that capture the details on 
mesoscopic level have been used by Khairallah et al. [67]. Using a multi- 
physics numerical simulation code ALE3D, the 3D simulation model 
captures the details of melt pool dynamics and relevant physical phe-
nomena such as fluid flow effects due to the recoil pressure, Marangoni 
effects, evaporation and radiative surface cooling while also considering 
the stochastic behavior of powder particles. This high-fidelity model 
explores in depth the physical mechanism in melt pools causing pore 
formations and suggested variations in laser power and appropriate 
hatch spacing to eliminate the pores. Ahmadi et al. [66] developed finite 
element frameworks using commercial code Abaqus that included both 
the grains and melt pools in the material. The model shown in Fig. 20(a) 
predicted pores for predetermined melt pool sizes. The work established 
a correlation between melt pool, porosity and resulting mechanical 
properties. It was found that the increases in laser power creates bigger 
melt pools, and results in higher densification leading to better me-
chanical properties as illustrated in Fig. 20(b), whereas high scan speed 
Fig. 24. Image of grains comparison between experiment and MC simulation 
for Ti64 material using Electron Beam Melting process [70]. 
Fig. 25. Comparison of grain structure from CA simulation and experiments [79]. 
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Journal of Manufacturing Processes 75 (2022) 415–434
429
Fig. 26. The phase field simulated dendrite morphologies [80] for different solute diffusion coefficient (D) and solid-liquid interface temperature T; formation of 
dendrite side arms is seen in (b) and (c). 
Table 1 
Summary of the process parameters collected from literatures for high densification of SS316L in L-PBF process, ‘-‘indicates the information not reported in the 
literature. 
Author L-PBF Machine 
Type 
Powder 
Size 
(μm) 
Spot 
dia 
(μm) 
Power 
(W) 
Speed 
(mm/ 
s) 
Hatch 
spacing 
(μm) 
Layer 
thickness 
(mm) 
Energy 
density (J/ 
mm3) 
Specimen 
geometry 
Highest density 
reported (%) 
Tucho et al. [7] SLM 280-HL – – 150 781 80 30 80 10 mm cube 99.86 
Yakout et al. [8] EOSINT M280 
SLM 
75 – 250 800 100 40 78 10 mm cube 98 
Kamath et al. [10] Concept Laser 
M2 
– 54 400 1900 105 30 67 10 mm × 10 mm 
× 7 mm 
(pillars) 
99.5 
Cherry et al. [13] Renishaw 
AM250 
15–45 70 180 400 124 50 104.5 10 mm cube 99.62 
Liverani et al. [14] SISMA MYSINT 
100 
– 50 150 700 70 20 153 – 99.9 
Sun et al. [15] SLM 
250 HL 
– 80 380 2000 35 50 109 10 mm cube 99.87 
Shi et al. [21] AM400 
Renishaw plc 
5–41 70 400 125 240 250 53 5 mm × 5 mm ×
10 mm 
(pillars) 
99.99 
Wang et al. [23] In-house 
Dimetal-280 
25 70 150 300 60 35 238 15 mm cube 98.5 
Salman et al. [24] SLM 250 HL-SLM 
Solution 
36 – 175 668 120 30 73 40 mm × 10 mm 
× 40 mm 
(pillars) 
99.9 
Yusuf et al. [25] Concept Laser 
M2 SLM 
15–40 – 200 1600 – 50 – 8 mm Cube 99.18 
Yasa et al. [28] Concept Laser 
M3 
– 180 85 200 144 30 98 – 99.9 
Krol et al. [46] Renishaw AM 
125 
15–45 35 125 480 10 50 174 20 × 20 mm 
(Thin wall 
specimens) 
99.95 
Kurzyn–owski et al. 
[47] 
SLM Realizer II 
250 
(MPC-HEK) 
20–63 200 200 220 – 50 – 10 mm × 8 mm ×
6 mm 
(pillars) 
99.8 
Choi et al. [48] Mlab-Cusing 
Concept Laser 
10–45 110 100 800 80 25 58.4 10 mm cube 99.2 
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results in decreasing number of melt pool, balling defects and hence 
reduced properties. 
6.2. Simulation case study for predicting high densifications 
A sample simulation case study to predict densification for various 
laser power/speed combinations is represented in this section using the 
commercial tool ANSYS ADDITIVE (2020R1). The objective of this 
simulation is to investigate the optimal combinations of laser power and 
scanning speed required to attain higher densification (above 99%) in 
SS316L material cubic samples. The version of ANSYS used predicts 
“lacks-of-fusion” porosity for the given set of L-PBF process parameters 
on the basis that as long as the process temperature is lower than melting 
temperature of the metal, the material will not be printed. Currently, 
other types of porosities from keyhole formations and balling effects are 
not predicted in this FEM module. In order to filter out power/speed 
combinations that produce unstable melt pools and unfit for attaining 
high densifications, a dimensional criteria based on melt pool di-
mensions is adopted [68]. Fig. 21 shows the melt pool dimensions used 
in criteria, the median depth (d) refers to the actual melt pool depth 
minus the layer thickness, or the melt pool depth starting from the 
bottom of the first layer. Similarly, the median width (w) and median 
length (l) refer to the width and length of the melt pool at the bottom of 
the first layer (the start of the substrate) respectively. The median di-
mensions are preferred instead of the average melt pool dimensions as 
the average values are skewed at the beginning of track when the melt 
pool is not entirely stable. The suggested criteria's is based on following 
three conditions: (i) the median melt pool depth (d) should reach at least 
halfway through the third solidified layer below for sufficient bonding, 
since our layer thickness is 40 μm (0.04 mm), we need the melt pool 
depth of at least 0.1 mm, which corresponds to median melt pool depth 
of 0.06 mm, (ii) median depth (d)-to-width (w) ratio should be below 
0.95 as to avoid excessive penetration of melt pool into solidified layers 
which otherwise may lead to keyhole formations, (iii) median length (l)- 
to-width (w) ratio should be below 4.0 so that melt pool is not elongated 
too long to cause balling effect usually associated with high power and 
speeds. For predicting density, a build of 3 mm side cube is simulated for 
various combinations of laser power and scanning speed as indicated in 
Fig. 22 with a hatch spacing of 100 μm and layer thickness of 40 μm. The 
simulation approach is done in two steps, (i) First, a single-track bead 
deposition of length 3 mm is simulated to evaluate the occurring melt 
pool dimension for all power/speed combinations and the combinations 
which attain unstable melt pool are filtered out, (ii) Densification 
simulation is then carried out on a 3 mm side cube using the good- 
candidate power-speed combinations, hence avoiding large iterations 
and narrowing the inputparameters window. 
Table 2 shows melt pool geometry dimensions attained from single 
track deposition simulation for few of the parameter combinations 
iterated, the laser power/speed combination highlighted in bold are the 
ones that satisfy the stable melt pool criteria and are selected as 
candidates for simulating densifications. Densification values predicted 
for the selected power/speed combinations is shown in, simulation 
predicts nearly a fully dense sample (~99.9%) for the good-candidate 
parameter combination selected based on stable melt pool dimen-
sional criteria. Also, the laser energy density values (68 W/mm3 to 75 
W/mm3) for this combination lie in the range optimum range reported 
in various experimental works for attaining high densification values 
[8,10]. 
Similar studies using simulations may be done to see the effect of 
varying hatch spacing on densification. Fig. 23 shows an example of 
densifications predicted by varying hatch spacing from 100 μm to 140 
μm for various power/speed combinations. It can be observed that 
increasing hatch spacing creates insufficient melt pool overlap resulting 
in poor bonding between adjacent scan tracks and thus decreasing 
densifications. It can be also observed that higher hatch spacing up to 
130 μm may be used for achieving high densification above 99.5% but 
with comparatively lower combination of power (200 W) and speed 
(500 mm/s), the lower speed creates sufficient time for heat buildup 
resulting in proper melting and bonding of material. Also, if increase in 
build rate is desired then a comparatively higher value power (300 W) 
and speed (900 mm/s) may be used with a hatch spacing of 120 μm for 
attaining higher densifications. Thus, the simulation approach enables 
us to make informed decisions about the optimum range of process 
parameters that may be used beforehand and hence minimize lengthy 
experimentations. 
6.3. Microstructural modeling 
Experimentally, it is only possible to investigate the microstructure 
after the part is finally printed, whereas accurate and effective compu-
tational modeling of the microstructure can give insight into grain for-
mation, dislocation cell evolution, pores evolution, etc. during the L-PBF 
process itself. This section reviews the various studies conducted on 
computational modeling of microstructural evolution during L-PBF 
process, however, we find that there is very limited work done on 
microstructural computational modeling of SS316L materials and there 
is a research gap on this alloy. Therefore, available microstructural 
computational frameworks on other alloys have been included to serve 
as a guide and may be applied to SS316L material. 
Table 2 
Simulated melt pool dimensions for single track deposition (3 mm), the values in bold satisfy the criteria for stable melt pool (d > 0.06 mm, d/w < 0.95, l/w < 4.0). 
Laser Power 
(W) 
Scanning Speed 
(mm/s) 
Median Melt pool (d) > 0.06 (d)/(w) < 0.95 (l)/(w) < 4.0 
Length, (l) 
(mm) 
Depth, (d) 
(mm) 
Width, (w) 
(mm) 
250 1100 0.349 0.049 0.105 – 0.467 3.324 
300 1500 0.455 0.052 0.097 – 0.536 4.691 
300 1300 0.427 0.054 0.107 – 0.505 3.991 
300 1100 0.431 0.065 0.111 0.065 0.586 3.883 
200 700 0.295 0.066 0.113 0.066 0.584 2.611 
250 900 0.367 0.066 0.112 0.066 0.589 3.277 
150 500 0.229 0.067 0.113 0.067 0.593 2.027 
350 1500 0.546 0.068 0.106 – 0.642 5.151 
350 1300 0.512 0.072 0.111 – 0.648 4.613 
*The table is truncated to show few of the iterations. 
Table 3 
Laser power/speed combinations that achieve higher densifications (>99.9%) in 
SS316L material cube of 3 mm side. 
Laser 
power 
(W) 
Scanning 
speed 
(mm/s) 
Hatch 
spacing 
(μm) 
Laser energy 
density 
(W/mm3) 
Simulated 
density 
(%) 
300 1100 100 68.2 99.98 
200 700 100 71.4 99.95 
250 900 100 69.4 99.96 
150 500 100 75.0 99.97 
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The literature available finds three methods commonly used for 
microstructural modeling namely; 1) Kinetic Monte Carlo (MC) method, 
2) Cellular Automation (CA), and 3) Phase Field (PF) modeling. The 
mathematical concepts and detailed comparison of the methods has 
been extensively discussed in the following literatures [69–72]. MC 
methods is a popular method already being used for prediction of grain 
evolution in casting [73,74], welding [75,76] and sintering process 
[77,78]. This concept of MC methods also has been extended to laser 
additive manufacturing as well. Sun et al. [45] conducted kinetic MC 
simulations using open-source code Stochastic Parallel Particle Kinetic 
Simulator to determine the influence of melt pool dimensions on 
resulting crystallographic orientation of SS316L. The work showed that 
desired crystallographic orientations can be successfully achieved by 
optimizing the melt pool dimensions using high laser power (400 W) and 
multi scanning strategy. Based on the simulation approach, it is reported 
that shallow melt pool promotes the formation of 〈001〉 crystallographic 
orientation which are typical of L-PBF process, while a short and deep 
melt pool favors a fine grain microstructure with 〈011〉 crystallographic 
orientation which enhances the tensile strength as well as ductility of the 
316L specimens. 
Rodgers et al. [70] used MC method to developed a model based on 
curvature driven grain growth mechanism to evaluate grain structure in 
the L-PBF process. The simulation model forecasted the order of grain 
size as a function of the process parameters and assesses the variations 
due to changing process parameters. The model incorporated pre- 
determined molten zone shape and heated effect zone geometry as 
representative of the process parameters such as power, speed and 
scanning pattern. The microstructural sensitivity to processing condi-
tions and flexibility of model for different types of metal additive process 
is successfully demonstrated by qualitative and quantitative compari-
sons with experimental observations, one such comparison of grain 
structure between experiment and simulation is shown in Fig. 24. 
Zinovieva et al. [79] combined Cellular Automata (CA) and finite 
difference methods to develop 3D model to predict the grain structure 
evolution in Ti64 alloy. The finite difference method is used to evaluate 
the transient temperature history and couple it to CA method for pre-
dicting the associated grain structure. The simulations revealed a course 
columnar grain structure and patterns of grain orientations in various 
plans across the built part. A comparison in Fig. 25 along a plane in build 
direction shows good agreement between simulated and experimental 
grain structure, the elongated grains orient along the build direction and 
grain boundaries are mostly being parallel to each other. It was also 
observed that microstructure evolution and the final grain structure is 
largely influenced by the shape of melt pool and scanning strategy, as 
the grains tend to grow in the direction normal to melt pool boundaries 
and the final grain structure tilts in the direction of melt pool movement. 
Phase field modeling is also another popular approach used for 
simulating microstructure in L-PBF process. Radhakrishnan et al. [80] 
also used phase field simulations to capture the dendrite formations in 
Ni based alloy. CFD simulations using ANSYS CFX (Released version 
16.0, ANSYS Inc.) is used to extract the temperature gradients and the 
solidification time inside a small representative volume. Using these 
inputs, the dendrite formation and micro segregation. 
profiles are predicted as shown in Fig. 26. The work confirmed the 
formation of predominantly columnar dendritic morphologyand high-
lighted a critical cell spacing value for formation of secondary dendrite 
side arms responsible for high strength [13]. The dendrite spacing is 
observed to be depended on the thermal gradient, solidification velocity, 
and solution diffusion coefficient which are influenced by the process 
parameters. Acharya et al. [81] determined the melt pool characteristics 
using computational fluid dynamics (CFD) employing free surface 
approach, which accurately captures melt pool dimensions. Using the 
results from CFD the phase field method is implemented using com-
mercial multi-physics code COMSOL to predict the associated grain 
formations. The model showed a strong influence of scan speed on 
dendrite grain orientations in IN718 samples. CALPHAD (Calculation of 
Phase Diagrams) software and phase field modeling has also been used 
to address the important issues related to the AM process such as ma-
terial chemistry, solidification including solidification path, phase for-
mation, microsegregation, solidification shrinkage, solidification 
cracking. CALPHAD software is also used to provide thermal expansion 
data used in FEA to determine residual stresses, thermal post processing 
for stress relieving and homogenization. Use of the above tools has been 
demonstrated in [82] where heat capacity and density were evaluated 
through Thermo-Calc software and were used as input in FEA thermal 
calculation for the AM process of alloy 625. CALPHAD tools have also 
been used in the study of AM process of Ti-Al-4V alloy. It was shown that 
heating and cooling cycles, encountered during AM, cause growth or 
dissolution of the α phase, depending on temperature. The resulting α 
and β morphologies have been studied by phase-field modeling in [83] 
indicating the formation of α colonies at grain boundaries. Finally, 
Thermo-Calc has been used in avoiding brittle phases in compositionally 
graded builds involving the joint between 304 stainless steel and Inconel 
625 alloy in [84]. 
6.4. Discussion - modeling for parameter optimization 
Most of the numerical modeling work reviewed focuses primarily on 
simulating thermal histories and melt pool dimensions. The L-PBF pro-
cess parameters such as power, speed, hatch spacing, and layer thickness 
directly influence the process dynamics and stability of the melt pools 
that are responsible for the properties of final printed part. For micro-
structural modeling, the transient temperature histories obtained by 
finite element models can be couple with various microstructural 
models to simulate the associated grain evolution. It is seen that the 
crystallographic texture, grain growth, size and orientation of the 
microstructure can be controlled by the L-PBF process parameters 
combinations. 
7. Summary and future prespective 
The paper reviews the various work available on L-PBF parameter 
optimization for 316L steel to attain highest densification and desired 
microstructure morphology. It is found that primary process parameters 
such as laser power, scanning speed, hatch spacing, layer thickness and 
scanning patterns have been extensively utilized to arrive on the opti-
mum energy density range to get highest build density. It is also un-
derstood that complete or 100% densification is rather difficult to 
achieve due to formation of gas pores at high temperatures and cannot 
be controlled merely by process parameters, nevertheless the overall 
defects may be minimized by using appropriate process parameter 
combination. We also observed that L-PBF process has been used to 
overcome the strength and ductility trade off limitation of the traditional 
manufacturing due to the flexibility offered by L-PBF process parameters 
to control the resulting microstructure thereby enhancing the mechan-
ical properties. The review also observed that studies involving process 
parameter optimization makes extensive use of numerical modeling and 
computer simulation techniques to study combined effect of process 
parameters on build quality which are rather difficult to be accom-
plished by experiments. The advanced numerical modeling techniques 
at both micro and meso level helps to establish process-structure- 
property relations for the L-PBF process. However, it is found that 
there is dearth of work on computational material modeling predicting 
microstructures of SS316L material from L-PBF process and large-scale 
modeling is rare to find due to the significantly high computational 
cost. These areas are being currently pursued as an important topic of 
research. Also, it is observed that with availability of wide spectrum of L- 
PBF machines and their different technologies makes it rather difficult to 
define standard set of process parameters even for common alloy such as 
SS316L, hence parameter optimization depends on the flexibility of the 
machine offered in terms of varying its process parameters. Also, quality 
of the build is a combined effect of process parameters involved and not 
N. Ahmed et al. 
Journal of Manufacturing Processes 75 (2022) 415–434
432
just that of a single parameter, hence investigations involving multi- 
parameter influence on the build using both experimental and numeri-
cal approaches is necessary for optimizing the L-PBF process. The review 
work also found that the dearth in research work to study variability of 
the stochastic process like L-PBF using the SS316L alloy on multiples 
machines. A framework could be developed in future works to analyze 
the potential manufacturing parameters to account for variability in the 
part dimensions and properties we observed in our discussions. The 
multitudes of process parameters involved in AM that influence the 
material's properties and these process-structure-property (PSP) re-
lationships has been addressed using experiments or multi-physics 
simulations. Though experimental methods are accurate, it is time- 
consuming to carry out such experimental trials. Also, multi-physics 
simulations can be time-consuming and expensive depending on the 
level of fidelity of the simulations in addition to the necessity for model 
calibration and validation. These methods generate large amount of data 
which needs to be deeply analyzed. Use of artificial intelligence (AI) and 
machine learning (ML) approaches will enable this critical need of 
effective and efficient data analysis and data mining for establishing 
detailed PSP relationships. ML models can be applied as surrogate 
models to assist process optimization by including multitudes of other 
process parameters along with primary parameters discussed in this 
paper, thus considering the combined effect of parameters rather than 
limiting the study to one or two parameters. This will also overcome the 
barrier of time and expense involved with experimentation and simu-
lations, whilst delivering accurate results that have the potential to 
accelerate L-PBF process optimization and application development 
cycles. 
Declaration of competing interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Acknowledgement 
The authors acknowledge the financial support provided by Khalifa 
University under Award No. RCII-2019-003. 
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