Off-axis high-speed camera-based real-time monitoring and simulation study for laser powder bed fusion of 316L stainless steel

Abstract

In-situ monitoring and post-process metrology form a basis to better understand the fundamental physics involved in the Laser Powder Bed Fusion (LPBF) process and ultimately to determine its stability. By utilizing high-speed imaging, various process signatures are produced during single track formation of 316L stainless steel with various combinations of laser power and scan speed. In this study, we evaluate whether these signatures can be used to detect the onset of potential defects. To identify process signatures, image segmentation and feature detection are applied to the monitoring data along the line scans. The process signatures determined in the current study are mainly related to the features like the process zone length-to-width ratio, process zone area, process zone mean intensity, spatter speed and number of spatters. It is shown that the scan speed has a significant impact on the process stability and spatter formation during single track fusion. Simulations with similar processing conditions were also performed to predict melt pool geometric features. Post-process characterization techniques such as X-ray computed tomography and 2.5-D surface topography measurement were carried out for a quality check of the line track. An attempt was made to correlate physics-based features with process-related defects and a correlation between the number of keyhole porosities and the number of spatters was observed for the line tracks.

Full text

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Off-axis high-speed camera-based real-time
monitoring and simulation study for laser powder
bed fusion of 316L stainless steel
Aditi Thanki (  aditi.thanki@kuleuven.be )
KU Leuven: Katholieke Universiteit Leuven
Carlos Jordan
Brian G. Booth
Dries Verhees
Rob Heylen
Mariam Mir
Abdellatif Bey-Temsamani
Wilfried Philips
Ann Witvrouw
Han Haitjema
https://orcid.org/0000-0001-7955-875X
Research Article
Keywords: Laser Powder Bed Fusion (LPBF), Real-time monitoring, Defect formation, Spatter analysis,
Finite element method, X-ray computed tomography (X-CT), Surface topography
Posted Date: September 28th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-2081606/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

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Abstract
In-situ monitoring and post-process metrology form a basis to better understand the fundamental physics
involved in the Laser Powder Bed Fusion (LPBF) process and ultimately to determine its stability. By
utilizing high-speed imaging, various process signatures are produced during single track formation of
316L stainless steel with various combinations of laser power and scan speed. In this study, we evaluate
whether these signatures can be used to detect the onset of potential defects. To identify process
signatures, image segmentation and feature detection are applied to the monitoring data along the line
scans. The process signatures determined in the current study are mainly related to the features like the
process zone length-to-width ratio, process zone area, process zone mean intensity, spatter speed and
number of spatters. It is shown that the scan speed has a signicant impact on the process stability and
spatter formation during single track fusion. Simulations with similar processing conditions were also
performed to predict melt pool geometric features. Post-process characterization techniques such as X-
ray computed tomography and 2.5-D surface topography measurement were carried out for a quality
check of the line track. An attempt was made to correlate physics-based features with process-related
defects and a correlation between the number of keyhole porosities and the number of spatters was
observed for the line tracks.
1. Introduction
In laser powder bed fusion (LPBF), melt pool ow dynamics are driven by surface tension gradients
resulting from strong temperature gradients beneath the laser in the melt pool region [1]. This complex
hydrodynamic ow sometimes leads to melt pool instability and generates features that can be observed
by sensors in real-time. Melt pool, vapor plume, and spatters are the key features observed in the LPBF
process when using a camera-based monitoring system [2, 3]. These features can provide signicant
information for understanding the LPBF process. The laser-metal powder interaction leads to formation
of a pool of molten metal known as the melt pool. It is well known that the build part properties are
signicantly inuenced by the melt pool geometry and dynamics [4]. Real-time monitoring of the melt
pool in LPBF has enabled the elucidation of these process phenomena to detect any unpredicted faults
and control the process parameters [5, 6]. The vapor plume is another feature that sometimes can be
observed in the monitoring data. The build chamber also encloses metal vapor plumes and a vapor jet
ejected from the melt pool due to laser melting. Though the amount of metal gas is relatively small, still
the concentration above the melt pool can be signicant and its concentration varies depending on
processing parameters. When the metal plume and vapor jet absorb the laser input, the metal gas could
go into an ionization stage and result in a plasma [7]. The laser absorption into this plasma further
reduces the laser power arriving at the powder and affects the laser absorption for the powder in the
powder bed. A shielding gas such as argon is used to move the metal plume and vapor jet away from the
melt pool, but the extraction can never be 100%.
Spatter refers to the molten metal droplets ejected from the melt pool during the melting of metal powder
[8] (Fig.1). The presence of spatters during the process may be a signature of an unstable process and

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the number of spatters may be an indicator of the occurrence of certain types of defects [9–11]; therefore
spatter features are extracted from the monitoring data. Different spattering mechanisms lead to
different types of spatter morphology. As mentioned by Young et al. [12], two main mechanisms are
responsible for spatter formation: recoil-pressure driven spatter, and entrainment-driven spatter. When the
temperature of the melt pool exceeds the boiling temperature, a strong metal evaporation happens.
Strong metal evaporation causes a high recoil pressure that exerts downward pressure on the melt pool,
resulting in a rapid uid convection there and sometimes causing the ejection of molten metal from the
melt pool. On the other hand, entrainment of powder particles occurs by a local pressure difference of
ambient gas ow due to the Bernoulli effect [13, 14]. Spatter usually occurs when high laser power, high
layer thickness, or low laser scanning speed are used. Spatter particles sometimes undergo an oxidation
reaction during in-ight cooling, which may therefore form an inclusion in the build parts [15, 16]. Spatter
may also land on a build part and cool down faster than the build part, thus forming a different
microstructure than the build part. These droplets increase the layer thickness and surface roughness of
the top layer [17, 18] while also enhancing process-induced porosity [19, 20]. Collisions between spatter
particles can further affect the particle dynamics [21]. The spatter direction and spatter velocity also
provide useful information and are considered as key physics-based image features in defect detection.
According to Ji and Han [22], the spatter direction may be a useful feature for identifying pore formation.
Processing parameters, material density, and the direction and amplitude of inert gas ow across the
powder bed all affect the spatter ejection trajectory. The spatter velocity is inversely proportional to the
material density, e.g., dense materials such as steel have spatters moving at lower velocity as compared
to spatters of relatively less dense materials such as titanium.
The characterization of process-induced spatter has been the focus of many studies. Bayle and
Doubenskaia measured the spatter ejection for stainless steel 904L by an infra-red camera and pyrometer
[24]. These spatters were observed to be ejected in the laser scanning direction (i.e. in the direction of
higher temperature gradient) and their velocity was in the range of 0.5–5 m/s. Qiu et al. [25] studied the
effect of scanning speed and layer thickness on the melt ow behavior. High speed imaging was
conducted at 10,000 frames per second using a high speed camera. The velocity of spatter particles was
found to increase with increasing scanning speed. Lane et al. [26] designed an off-axial setup with a 45°
viewport and used a camera for measuring lower temperature phenomena. Although a measurement of
smaller particles was not possible due to the optical resolution limit, some spatter particles of 200 µm
diameter could be observed being ejected from the melt pool. Matthews et al. [27] observed the depletion
of powder particles (i.e. denudation) around the solidied track using ultra-high-speed imaging at 500k
frames per second with an optical resolution of about 5 µm. Ly et al. [28] carried out high-speed imaging
of melt pool dynamics similar to Matthews et al. [27] for 316L stainless steel tracks produced on top of a
metal substrate and compared the experimental results with simulations. The authors concluded that
15% of the spatters are recoil pressure-induced droplets with velocities of 3–8 m/s. Gunenthiram et al.
[29] performed single track experiments similar to Matthews et al. [27], but with a powder bed that is
moved with a high speed (x, y) table below the laser. Repossini et al. [30] recorded spatter formations for
different melting conditions using an off-axial high-speed camera at 1000 frames per second. Repossini

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et al. [30] observed a higher number of spatters in the over-melting conditions as compared to the normal
melting conditions. Zhao et al. [31] used high-speed X-ray imaging and diffraction to image the melt pool
dynamics of Ti-6Al-4V at 50k frames per second. The keyhole porosity formation dynamic could be seen
with high spatial and temporal resolutions.
However, while a lot of study is already done regarding real-time LPBF observation, only a few studies are
performed that correlate the resulting signature features to defect formation [3]. This paper discusses a
feature-based real-time optical monitoring system and the effect of processing parameters on both the
optical features and defect formation. For that purpose, an off-axis high-speed camera-based monitoring
setup was used to measure melt pool, vapor plume, and spatter features. Spatter features along the line
track were automatically identied from post-process 2.5-D surface topography measurements. The melt
pool geometry was predicted from a nite element model and later compared with the experimental
results. Also, a correlation analysis was performed between the optical features extracted from the
monitoring data and the quality features observed in the X-CT reconstruction of the line tracks. The
structure of the paper is as follows. First the experimental details are given in section 2. Next, an overview
of the experimental results is discussed in section 3, followed by the conclusions in section 4.
2. Experimental Procedure
2.1 LPBF process
To provide insights into defect and feature formation, an experiment was performed by printing single
tracks of 316L Stainless steel (316L) on top of an LPBF 316L substrate (Fig.2). The substrate of 9 mm ×
9 mm × 7.5 mm was printed on a 3D Systems ProX DMP320 machine with nominal processing
parameters (laser power: 215 W, laser speed: 900 mm/s). The LPBF machine’s laser source was an
Ytterbium bre with wavelength of 1064 nm and the laser spot size was 75 µm. The process was carried
out using argon as a shielding gas. The powder particle size distribution of the 316L Stainless steel
powder from 3D Systems ranges between 20–50 µm. In total, 14 line scans were printed on top of the
substrate with seven different process parameter sets (Table1). A layer thickness of 30 µm and a hatch
spacing of 100 µm was used for both the substrate and the line tracks.
Seven variations in the energy input were accomplished by either changing the laser power or the
scanning speed (Fig.3). These are summarized in Table1. The linear energy density (LED) was
calculated for all settings by dividing the laser power (
P
) by the scanning speed (
v
). Line scans were
printed twice in opposite directions for every single combination of processing parameters. This step was
done to check the reproducibility and to investigate the effect of the gas ow direction. The length of the
line track was 8.8 mm and the distance between the line tracks was 0.5 mm. Prior to printing the line
scans, the substrate layer below the line scans was remelted without recoating to reduce the surface
roughness and to remove pores (if any) from the layers below. As a result, all the defects that form
underneath the line tracks can be considered as originating from the line tracks themselves. The test
object was cut by wire-electrical discharge machining (wire-EDM) from the baseplate after processing.

Page 5/29
Table 1
Process parameter for single tracks of 316L Stainless steel.
ID Laser
power
(P)
/
W
Scanning speed
(v)
/ mm·s-1
Linear energy
density (LED) / J·m-1
Condition
1 215 900 240 (100%) Nominal processing parameters
2 215 600 360 (150%) High linear energy density
3 215 450 480 (200%)
4 215 225 960 (400%)
5 500 523 960 (400%)
6 500 2093 240 (100%) Energy input same as nominal, but
higher power and speed
7 54 900 60 (25%) Low linear energy density
2.2 In-process monitoring and image processing
Monitoring data were recorded by using an off-axis high-speed camera (Mikrotron Eosens 3CL) placed
with an inclination angle of about 25° with respect to the normal of the build plate. The advantage of
using an off-axis high-speed camera-based system is being able to monitor the spatter signature and not
requiring any modications in the machine or the optical path of the laser beam. The video stream is
acquired at 20,000 frames per second, with each frame having 120 × 120 pixels. The camera had a 30 µs
exposure time, and a 50 mm lens with an aperture of f/16. The aperture and shutter time were set
manually so that spatters are clearly visible in the video stream. The resulting pixel size corresponds to
approximately 100 µm × 100 µm over a eld of view of 12 mm × 12 mm. A short-wavelength pass lter
with a cut-off value of 975 nm was added before the camera to protect it from the laser source. The
monitoring system captured radiations in the visible to near infrared wavelength range between 350 nm
and 975 nm. The LPBF machine was equipped with the Materialise Control Platform (MCP) and controller
data was also collected at 100 kHz. This controller data consisted of the (x,y)-position of the laser on the
build area, the laser on/off signal, the laser power, the laser speed, and timestamps. The high-speed
camera was connected via Camera Link to NI PXI. Data was stored to an NI SSD drive with a high-speed
data transfer rate of around 1.5 GB/s.
Physics-based image features were identied from the monitoring data after binarization of the image
frames. A static threshold of 40 intensity units is applied to the 8-bit video frames (whose values range
between 0 to 255). The motivation for using this static threshold is because the resulting image has a low
level of noise and the appearance of the environment in the eld of view remains consistent over time.
The melt pool, vapor plume, and spatter regions have a higher intensity than the background and
therefore no sophisticated method is used for segmentation. After binarization, the image is segmented
automatically per frame into background, a combined melt pool & vapor plume which is labelled ‘process

Page 6/29
zone’ in this work, and spatter regions by using a connected component analysis algorithm (Fig.5). From
the segmented images, several established optical features were extracted: process zone length-to-width
ratio, process zone area, process zone mean intensity, and number of spatters [2]. Additionally, spatter
speed and spatter direction were also computed. A detailed discussion of individual features is presented
in section 3.
To identify the process zone and spatters on each frame, connected component labeling is used [32] to
uniquely label each high intensity region within the eld of view on the off-axis camera. The process zone
is taken to be the largest of these regions while the remaining regions are labeled as spatter. The
trajectory for each spatter is then calculated by matching its position in neighboring frames using the
Euclidean distance: a spatter in one frame is matched to the closest spatter in the subsequent frame.
Once a spatter cools down or disappears from the eld of view of the camera, the tracking of the spatter
is completed and the spatter’s velocity and direction are computed. The velocity of a spatter is derived
from its total distance traveled (in meters) divided by the number of frames in which the spatter appears.
To measure the direction of a spatter, a linear vector, is t to the spatter’s full trajectory and the angle
between the spatter and the laser scanning direction is computed as follows:
1
where, represents the coordinates of the melt pool trajectory vector during the frames
where the spatter is within the eld of view of the camera, and represents the
coordinates of the trajectory vector of the spatter. The area of the spatter is calculated as the maximum
pixel count from all the frames where the spatter appears, capturing the moment where the spatter
appears the closest to the camera with the highest luminous intensity. The spatter number is calculated
per frame as the total number of the labeled spatters obtained from the connected component labeling.
Also, the mean number of spatters per line track is calculated. All other process zone and spatter features
were calculated as described in [2].
2.3 X-ray computed tomography (X-CT)
The test object was scanned by X-ray computed tomography (X-CT) using a Nikon XT H 225 ST machine
at KU Leuven that has a maximum tube potential of 225 kV. The instrumental parameters were set at a
tube voltage of 220 kV, 45 µA, and an exposure time of 4000 ms. A total of 3600 radiographs were taken.
The chosen target material for the X-ray generation was tungsten. The source-to-detector distance was
1048 mm and the source-to-object distance was 52 mm, leading to a measurement resolution (voxel size)
of 10 µm × 10 µm × 10 µm. A tin lter of 0.5 mm thickness was used in front of the X-ray tube window to
reduce the beam hardening effect. The X-CT volume reconstruction was performed using the Feldkamp-
Davis-Kress (FDK) algorithm [33].
vSp
,
θ
θ
=
tan
−1 −
tan
−1
vMP
(
y
)
vMP
(
x
)
vSp
(
y
)
vSp
(
x
)
vMP
(
y
),
vMP
(
x
)
vSp
(
y
),
vSp
(
x
)

Page 7/29
From the X-ray computed tomography images along the line tracks described in section 2.1, several
surface and sub-surface process-related defects were found. Based on these results, process parameters
were either labelled as belonging to a ‘defect’ or ‘no-defect’ region. Line tracks with laser parameters
P
=
215 W and
v
= 900 mm/s (ID#1) were marked as having the optimum processing parameters as no
defects were observed along the track (Fig.6a). A series of keyhole porosities were formed below the two
line tracks with laser parameters
P
= 215 W and
v
= 225 mm/s (ID#4) and these were therefore marked as
keyhole porosity regions. As the resolution of X-CT reconstruction resulted in a 10 µm voxel size, it was
possible to measure keyhole pore sizes from 10 µm and above. With laser parameters
P
= 54 W and
v
=
225 mm/s (ID#7), incomplete melting of the two line tracks was observed and, therefore, these tracks
were labelled as tracks having lack-of-fusion defects. Discontinuous line tracks were observed for the two
tracks with laser parameters
P
= 500 W and
v
= 2093 mm/s (ID#6) due to Plateau-Rayleigh instability.
While the energy input for both the optimum and the Plateau-Rayleigh instability conditions were the
same, defects form at higher power and speed driven by surface tension in the melt pool region resulting
in discontinuous melt tracks. It can be seen in Fig.6 that lack-of-fusion and Plateau-Rayleigh instability
result in surface defects, whereas a keyhole porosity is a sub-surface defect that occurs several layers
below the line track.
2.4 2.5-D surface topography measurements
After performing the X-CT scan, the surface topography of the top surface of the test object was
measured by a 2.5-D confocal optical microscope (S neox - Sensofar) in order to identify spatter on the
line tracks. A stitched area of 8.33 mm × 7.26 mm was measured with a pixel size of 0.65 µm using an
objective giving 20x magnication and having a numerical aperture (NA) of 0.45. After measurement, the
topography was pre-processed by removing spike-like artifacts and subtracting the least-squares plane.
Spatter particles were identied along the line track using cross-correlation with a typical 70 × 70 pixels
spatter feature kernel.
2.5 Finite element modeling of the melt pool
A nite element model was developed to simulate the production of a single track of 316L Stainless steel
using AdditiveLab software [34]. In this study, a rod and rotary Gaussian prole is modeled as a moving
heat source. The mesh size varies from 8 µm to 100 µm. Conductive heat losses were considered to the
side, the bottom, and the top of the simulation domain. In addition, the radiative heat losses were
considered at the top of the simulation domain. The inuence of process parameters on the melt pool
dimensions were evaluated. To do so, melt pool dimensions for different process parameters were
predicted and validated experimentally. The calculation time for this problem is approximately 1–10 min
on an 8-core machine. Therefore, it can provide a rough estimation of the melt pool dimensions in a fast
way, avoiding the need of longer calculation times. The used thermophysical properties of 316L Stainless
steel are given in Table2.

Page 8/29
Table 2
Thermophysical properties of 316L Stainless steel [35].
Property Symbol Unit Employed value
Density
ρ
kg/m37950
Heat capacity
Cp
J/kg·K 480
Thermal conductivity
k
W/m·K 15
Evaporation temperature
Tv
K 3073
Melting temperature
Tm
K 1683
Substrate temperature
T0
K 573
Absorptivity
A
- 0.5
2.6 Metallography and optical microscopy
Once the X-CT and 2.5-D surface topography measurements are performed, the test object is prepared for
metallography. The object was resin mounted, grinded, polished, and etched to measure the melt pool
width and depth. These width and depth measurements can be used amongst others to validate the
simulated results of the line tracks. Electrolytic etching was performed with 10% oxalic acid to etch the
316L sample cross-section.
3. Results And Discussion
Physics-based image features related to process-related defects are studied and discussed in this
section. As the resolution of the off-axis camera does not allow to individually detect the melt pool and
the vapor plume, both are combined in an area that is labelled the ‘process zone’. The rst part of this
section discusses the process zone features extracted from off-axis camera monitoring for the line track
experiment, specically process zone area, process zone length-to-width ratio, and process zone mean
intensity. The second section presents the spatter features segmented from raw images, specically the
number of spatters, spatter speed, and spatter direction. The third part shows the results of automatic
spatter identication from 2.5-D surface topography measurements. The fourth subsection presents melt
pool geometry features such as depth and width extracted from both nite element modeling and optical
microscopy of the line tracks cross-section. In the nal part, a correlation between signature features
extracted from real-time monitoring data and X-ray computed tomography for the line track is discussed.
3.1 Melt pool and vapor plume feature analysis from
monitoring data

Page 9/29
Several process zone (combined melt pool and vapor plume) features are extracted, measured in the XY
plane, and discussed in the current section for the line tracks. The process zone dimensions such as area
and length-to-width ratio are of major importance in LPBF since these dimensions have a large inuence
on the process stability. The process zone area and length-to-width ratio depend on the processing
parameters being used (laser power, scanning velocity) and on the thermal diffusivity of the material
being processed. The melt pool will be more elongated (i.e., a larger length-to-width ratio) for the material
with smaller thermal diffusivity, whereas it will lead to a more equiaxed melt pool (i.e., a length-to-width
ratio closer to 1) for the material with a high thermal diffusivity. On the other hand, the inuence of the
processing parameters on the process zone area and length-to-width ratio is rather complex. Laser power
and scanning speed are not interchangeable, i.e., the same energy density with different laser power and
scanning speed will lead to different results. To understand the effect of combinations of laser power and
scanning speed on process zone area and length-to-width ratio, line tracks with varying laser power and
scanning speed were produced as discussed above (Table1).
Figure 7 shows the mean value of process zone area along the different line tracks on the process
window that covers the seven combinations of laser power and scanning speed conditions that were
studied. It was observed that the process zone area was similar for optimum as well as keyhole porosity
conditions, whereas it varied for Plateau-Rayleigh instability and lack-of-fusion conditions. A signicant
inuence of laser power on the process zone area was observed. It is known that the laser power
inuences the temperature reached in the molten part and the powder bed since it determines the total
amount of energy added per second by the laser source to the powder bed. At higher laser powers,
process zone temperatures will be larger as compared to process zone temperatures when using low
laser power, therefore a higher process zone area was observed at higher laser power as compared to the
process zone area at lower laser power. It was observed from the monitoring data that conditions that
form Plateau-Rayleigh instability result in the highest process zone area, whereas the lowest process
zone area was observed for the lack-of-fusion conditions.
In contrast to the process zone area, quite different results for the process zone length-to-width ratio are
obtained for a combination of several laser power and scanning speed conditions. Here, a signicant
inuence of the scanning speed on the process zone length-to-width ratio was observed. The length-to-
width ratio increases with increasing scanning velocity, specically for the Plateau-Rayleigh instability
conditions. The possible reason for the resulting higher length-to-width ratio at the higher scanning speed
condition is that the amount of radiative heat loss is lower since there is simply not enough time to
radiate a signicant amount of energy. It is also possible that a higher laser speed will heat up a larger
area over the 30 µs that the camera is recording the image. At low scanning speeds, a large part of the
added energy is lost through heat radiation, which results in smaller melt zone dimensions. In this study,
a length-to-width ratio of 2.5 was observed for Plateau-Rayleigh instability condition, whereas a length-to-
width ratio of 1.5 was measured for optimum, keyhole porosity and lack-of-fusion conditions (Fig.8).
In general, any object radiates heat. By measuring this radiation, one can estimate the temperature of an
object. The melt pool emits thermal radiation at the wavelengths that correspond to the melting

Page 10/29
temperature (Planck's law). One way to estimate the temperature of the melt pool is by capturing the
intensity of the process zone [6, 36]. The grey value of a pixel in an image from a CMOS camera is a
function of the emitted light intensity. Here, a linear relation is taken between the process zone intensity
and the grey value (on an 8-bit grayscale). The mean value of intensity within the process zone is
calculated by taking an average of process zone mean intensity for all the frames along a line track of
8.8 mm. Figure9 presents the average value of the process zone mean intensity for the line tracks in the
process window.
For the optimum process parameters, heat transfer happens mainly via heat conduction, however, heat
transfer via heat radiation also takes place. For keyhole mode melting, convective heat transfer (also
known as thermo-capillary convection) is the most common form of heat transport. As mentioned earlier,
for the higher scanning speed such as the Plateau-Rayleigh instability condition, the heat transport via
heat radiation is lower. As can be seen from Fig.9, the process zone mean intensity value for optimum
condition is in the range of 91–96, and for Plateau-Rayleigh instability and keyhole porosity region, the
mean intensity of the process zone is in range of 86–91. Clearly the grey value decreases with decreasing
laser power such as for lack-of-fusion defects, since with lower laser power, the temperature of the
process zone is quite low. The inuence of laser power and scanning speed on the process zone mean
intensity seems complex and further research is required to characterize it.
3.2 Spatter feature analysis from monitoring data
In this section, the effect of laser power and scanning speed on spatter features is discussed, specically
the number of spatters, spatter speed, and spatter direction. It is known that molten metal is ejected from
the melt pool once the dynamic pressure, due to the recoil pressure and the Marangoni force, is higher
than the pressure produced by the surface tension [28, 37]. In this work, spatter features extracted from
raw monitoring data are of high intensity (similar to the process zone) and are therefore called “hot
spatter”. Moreover, powder spatters are ejected near the process zone and due to their low intensities,
these “cold spatter” cannot be recorded in our real-time monitoring data. The spatters move in three
dimensions, but in this work, we can only observe spatter particle motion and trajectory in the projection
on the XY plane parallel to the sensor of the camera. Some studies showed the feasibility of spatter
tracking using a high-speed stereo vision and 3D particle tracking velocimetry [38, 39].
From Fig.10, it can be seen that scanning speed has a signicant inuence on the spatter generation and
it will later be correlated with quality of the part in section 3.5. For the keyhole porosity condition, the
mean value of the number of spatters was approximately 1 for all the frames along a line track of 8.8
mm, whereas for all the other conditions, almost no hot spatter was observed along the line track. The
spatter that forms during the keyhole mode melting is because of the recoil pressure and the Marangoni
force as mentioned earlier.
In addition to the number of spatters, spatter speed and orientation might also provide useful information
of the process stability. On the macroscopic scale, the spatter ejection trajectory is possibly dominated by

Page 11/29
the vapor plume and the gas ow [13, 28, 40, 41]. Most of the droplet spatters are of 100–400 µm size as
calculated from the monitoring data. This is much larger than the size of the powder particles. The speed
of spatters was calculated by considering the spatter distance between two consecutive frames. The
calculated spatter speed ranged from ∼0.4 m/s to ∼7.8 m/s. The average spatter speed along the line
track was lowest with 0.2 m/s for the lack-of-fusion condition, and highest with value of 3.3 m/s for the
Plateau-Rayleigh instability condition. For the optimum and keyhole porosity conditions, the spatter
speed is almost similar with mean value of 1.4 m/s and 1.6 m/s per 8.8 mm line tracks, respectively.
Figure 11 presents the polar histogram of the spatter orientation for different laser power and scanning
speed conditions. Here, 0° represents the melt pool and laser scan direction and 90° represents the gas
ow direction. The radius in the polar histogram is the normalized amount of spatter (in pixels) along
each frame of 120 × 120 pixels. For the optimum condition, spatters are oriented to both gas ow
direction and backward to melt pool scan direction, whereas for both Plateau-Rayleigh instability and
lack-of-fusion conditions, spatters were observed moving mainly in the opposite direction of the moving
melt pool. A uniform distribution in all directions was seen for the keyhole condition, similar to what is
found in the literature [28]. Due to the vapor plume emission being constrained in the vertical direction by
the keyhole walls, Ly et al. [28] observed the spatters rising almost vertically in the keyhole mode melting
condition. Conversely, for the low energy input, the vapor plume emits mainly in the backward direction,
which causes droplet spatters to orient in the similar direction. Meanwhile, Zhao et al. [42] discovered a
new mechanism for droplet spatter in keyhole conditions based on the X-ray synchrotron imaging
technique. A tongue-like protrusion on the front keyhole wall undergoes a bulk explosion, which causes
spattering to occur. Consequently, the momentum released by the explosion of the protrusion determines
the spatter speed, and spatters are seen being ejected along both the front and the rear keyhole rims. This
is in agreement with the results obtained in the current study about spatter orientation in keyhole mode
melting.
3.3 Spatter identication from 2.5-D surface topography
measurements
Confocal microscopy was performed for post-process spatter analysis where several spatter features
were extracted from monitoring data. Several studies have already been performed to identify the spatter
features from surface topography measurements [43, 44]. In this work, spatters were automatically
identied along the line track via cross-correlation, where the kernel is a 70 × 70 pixels spatter feature
(Fig.12 (a)). The cross-correlation map shows spatters being identied along the line track (Fig.12 (c)). A
spatter map was extracted after applying a thresholding to the cross-correlation map (Fig.12 (d)). The
threshold value selected is 0.015. To conrm the number of spatters observed from monitoring data, a
spatter map is obtained from the surface topography measurements. A comparison of spatter maps for
keyhole mode melting and optimum conditions is made (Fig.12 (d) and (e) respectively), which conrms
that more spatter is identied along the line track for keyhole condition than for the optimum condition.

Page 12/29
3.4 Finite element modeling of melt pool
The relationship between the melt pool dimensions and the laser parameters (scanning velocity and laser
powder) were predicted using a nite element model and validated experimentally. Conservation of mass,
momentum, and energy are considered in order to model the melt pool and heat affected zone. The
model solves the heat equation considering conduction. 3D nite element modeling of LPBF for
conduction and keyhole modes has already been carried out as reported in the literature [45–47]. In this
work, the FEM simulation results seem to be in good agreement with the experimental data for 316L
stainless steel considering the experimental reproducibility and the numerical approximations (Table3).
Figure13 indicates the dimensions of the melt pool obtained experimentally and the temperature
distribution obtained from simulations. With
P
= 215 W and
v
= 600 mm/s, the melt pool cross-section
has a semi-circular shape, similar to the melt pool cross-section in conduction mode. By reducing the
scanning speed further to
v
= 225 mm/s, the melt pool depth increases due to high recoil pressure and
enters into keyhole mode. As can be seen from Fig.13, in a keyhole mode, the keyhole upper part is wide,
whereas the bottom region has a V-shape and is narrow.
Table 3
Experimental and simulated melt pool width and depth.
ID Laser
power (
P
)
/ W
Scanning speed
(
v
) / mm·s-1
Experimental
width / µm Simulated
width / µm Experimental
depth / µm Simulated
depth / µm
1 215 900 137 137 149 105
2 215 600 152 161 190 167
3 215 450 154 166 265 218
4 215 225 204 215 443 336
5 500 523 178 131 556 605
6 500 2093 149 114 204 240
7 54 900 0 0 0 0
3.5 Correlation between real-time monitoring and X-ray
computed tomography
In order to validate the signatures of process-related defects, it is crucial to correlate the part quality with
the real-time signatures from the monitoring. Both keyhole porosity and droplet spatter formation are
stochastic in nature. However, correlation between porosity and spatter was possible using the signal
recorded from real-time monitoring and X-ray computed tomography. By correlating the 2D monitoring
information of the process zone and spatter with 3D information of the surface and the subsurface
defect formation from X-CT, it could be possible to predict for future experiments the susceptibility for
defect formation just from the monitoring data. The correlation analysis between in-situ process

Page 13/29
signatures and post-process part characterization has already been studied by several authors [2, 3, 6,
48–50]. Among all the physics-based features discussed in previous sections, a strong correlation was
observed between the number of keyhole porosities below the line track and the number of spatters
recorded from the off-axis monitoring system. Correlating monitoring signature features to defects seen
in the X-CT is dicult for fully printed parts, therefore here feature extracted from the monitoring data is
presented for single tracks.
The spatter features’ formation depends mainly on material properties and the processing parameters. As
seen in section 3.2, both laser power and scanning speed have an impact on the amount of spatter
formation. An increase in energy input results in keyhole formation and in more spatter event formation
(Fig.15). A large number of spatter formations indicate a high recoil pressure in the melt pool. Compared
to the keyhole condition, less spatter formation is observed for conduction mode melting (Fig.14) and
therefore spatter formation can be related to the quality of LPBF part.
4. Conclusions
In this work, several physics-based features were extracted from off-axis high-speed camera-based
monitoring data, and process-related defects in LPBF were studied using X-ray computed tomography.
The process zone (melt pool and vapor plume) and spatter features were extracted from images and
discussed for line tracks with various laser power and scan speed conditions. A signicant inuence of
laser power on the process zone area, and scanning speed on process zone length-to-width ratio, were
observed. The highest process zone area and length-to-width ratio were observed for tracks having
Plateau-Rayleigh instability, a defect that forms under high laser power and scan speed conditions. The
inuence of laser power and scanning speed on the process zone mean intensity is not clear and further
research needs to be done. In addition, several spatter-based features were calculated after image
segmentation. Droplet spatter speed ranged from ∼0.4 m/s to ∼7.8 m/s for 316L stainless steel. A more
uniform spatter orientation was observed for keyhole mode melting conditions. To conrm the spatter
variations seen in the monitoring data, post-process surface topography measurements were performed
along the line track and spatters were segmented by cross-correlation template matching. The spatter
number segmented from surface topography measurements agrees qualitatively with the spatters
segmented from the monitoring data. Additionally, we experimentally validated a nite element model
that predicted the melt pool dimensions for several laser parameters. An attempt was made to correlate
physics-based features with process-related defects. A strong correlation was observed between the
number of keyhole porosities from X-CT and the number of spatters from real-time monitoring data. Once
these process signatures are conrmed for more materials and process conditions, it is possible to apply
real-time closed loop feedback control to control defects based on the features extracted from real-time
monitoring. In the future, physics-based features and quality measures will be studied for different part
geometries, materials and machine sets.
Declarations

Page 14/29
Author Contributions:
Aditi Thanki: conceptualization, methodology, investigation, formal analysis, visualization, validation,
writing - original draft; Carlos Jordan, Brian G. Booth: formal analysis, visualization, software, writing -
original draft; Dries Verhees, Rob Heylen: data curation, writing – review and editing; Mariam Mir: formal
analysis, software, validation, writing – review and editing; Abdellatif Bey-Temsamani, Wilfried Philips:
supervision, project management, funding acquisition, writing – review and editing; Ann Witvrouw:
conceptualization, supervision, project management, funding acquisition, resources, writing - original
draft; Han Haitjema: conceptualization, supervision, funding acquisition, resources, writing - original draft.
Funding:
This work is nancially supported by the VLAIO ICON project “Vision-in-the-Loop” (HBC.2019.2808)
(https://www.imec-int.com/en/what-we-offer/research-portfolio/vil) and by Flanders Make
(https://www.andersmake.be/en), a research centre on manufacturing industry. Flanders Make also
owns the additive manufacturing infrastructure, where the experiments described in this paper were
performed.
Acknowledgements:
The data presented in this paper was acquired as a part of “Vision-in-the-Loop” project. Authors want to
acknowledge project partners for the technical discussions, which include imec-Vision Lab, University of
Antwerp; AdditiveLab; Materialise; Dekimo Products and ESMA NV.
Ethics approval:
Approved.
Consent to participate:
The authors provide consent to participate.
Consent for publication:
The authors give consent for publication.
Conicts of Interest:
The authors declare no conicts of interest.
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Figures

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Figure 1
Spatter formation mechanism “after [23]”.
Figure 2

Page 19/29
The line tracks on the top surface of the test object, the length of each line track is 8.8 mm.
Figure 3
Processing parameters window for the single tracks of 316L Stainless steel (bubble diameter is
proportional to the linear energy density).

Page 20/29
Figure 4
Schematic of the off-axis high-speed camera setup mounted on the LPBF machine.

Page 21/29
Figure 5
Image segmentation method of process zone and spatter (a) raw image frame binarized at cut-off value
40, (b) melt pool & vapor plume (process zone) segmentation, (c) spatter segmentation.
Figure 6
X-ray computed tomography cross-section along the line tracks of (a) optimum processing parameters
(ID#1), (b) keyhole porosity (ID#4), (c) Plateau-Rayleigh instability (ID#6) and (d) Lack-of-fusion defect

Page 22/29
(ID#7).
Figure 7
Interpolated contour plot indicates the mean value of the process zone area along the line tracks. The
interpolation was done with a distance method.

Page 23/29
Figure 8
Interpolated contour plot about mean value of process zone length-to-width ratio along the line tracks.

Page 24/29
Figure 9
Interpolated contour plot about mean value of process zone mean intensity along the line tracks.

Page 25/29
Figure 10
Interpolated contour plot showing the mean number of spatters per 8.8 mm line track.

Page 26/29
Figure 11
Polar histogram showing spatter orientation with respect to the process zone for (a)
P
= 215 W and
v
=
900 mm/s (optimum), (b)
P
= 215 W and
v
= 225 mm/s (keyhole porosity), (c)
P
= 500 W and
v
= 2093
mm/s (Plateau-Rayleigh instability), (d)
P
= 54 W and
v
= 225 mm/s (lack-of-fusion).

Page 27/29
Figure 12
Spatter segmentation by correlation from surface topography measurements along the line track (keyhole
condition) (a) spatter kernel used for cross-correlation, (b) surface topography as measured by confocal
microscopy, (c) cross-correlation spectrum between (a) and (b), (d) spatter identication by applying a
threshold on (c), (e) spatter identication for optimum condition.

Page 28/29
Figure 13
Comparison of experimental and simulation results for melt pool dimensions of
P
= 215 W and
v
= 600
mm/s (left),
P
= 215 W and
v
= 225 mm/s (right).
Figure 14

Page 29/29
The number of spatters from off-axis monitoring and X-CT cross-section of line track with
P
= 215 W and
v
= 900 mm/s.
Figure 15
The number of spatters from off-axis monitoring and X-CT cross-section of line track with
P
= 215 W and
v
= 225 mm/s.
Supplementary Files
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oatimage1.png