Additive Manufacturing 69 (2023) 103540
Contents lists available at ScienceDirect
Additive Manufacturing
journal homepage: www.elsevier.com/locate/addma
Roughness measurements across topographically varied additively
manufactured metal surfaces
Alex Mirabal a, Ilker Loza-Hernandez a, Courtney Clark a, Daniel E. Hooks a, b, Michael McBride a,
Jamie A. Stull a, *
a
b
SIGMA-2: Finishing Manufacturing Science, Los Alamos National Laboratories, Los Alamos, NM 87544, USA
MPA-CINT: Center for Integrated Nanotechnologies, Los Alamos National Laboratories, Los Alamos, NM 87544, USA
A R T I C L E I N F O
A B S T R A C T
Keywords:
Additive Manufacturing
Roughness
Arithmetical Mean Height
Optical Microscopy
Physical Profilometry
Contact Stylus
Laser Scanning Confocal Microscopy
Coherence Scanning Interferometry
Scanning White Light Triangulation
Characterization of surfaces for additively manufactured (AM) metals are only valuable to the extent that they
can be reliably related to performance properties and compared to other surfaces, including both AM and
traditional manufactured surfaces. In this work we aim to define best practices for precise, comparable surface
roughness measurements of representative metal AM parts. These parameters are broadly dependent on: 1) in­
strument, 2) acquisition parameters, and 3) analysis methods. AM surfaces uniquely span a large range in
roughness, from extremely rough as-printed down skins (>25 µm for powder based AM), to mirror-like surfaces
after polishing (<1 µm), requiring different considerations. This work outlines the decision process for measuring
surface roughness of laser powder-bed fusion AM 316 L stainless steel samples, detailing the required sample size
as well as acquisition parameters, such as the z-range. The results are compared across four instruments using
coherent scanning interferometry, laser scanning confocal microscopy, structured white light triangulation, and
physical profilometry. Comparisons across differing measurement techniques demonstrates the broad applica­
bility of the suggested parameter spaces. Recommendations for qualifying measurements acquired using different
instruments, not explored in this work, will be provided. This will allow community-wide comparison of areabased surface topographical measurements, beyond the linear measurement standards suggested in ISO
21920-3-2021.
1. Introduction
Metal additive manufacturing (AM) holds promise as a trans­
formative technological advancement with impact across nearly every
industrial sector. The ability to rapidly print parts with internal features,
complex geometries, and near-net-shape is advantageous compared to
traditional metal fabrication technologies. Process-structure-property
relationships of metal AM parts have received significant attention in
terms of traditional bulk mechanical/functional requirements [1–7].
However, significant development is still required for AM part certifi­
cation, including the corrosivity and surface roughness [8]. To exem­
plify, Web of Science returns over 27,000 articles since the year 2000
when searching for “Metal Additive Manufacturing”; this number drops
to just over 1400 when “surface roughness” or “roughness” is added to
the search term (Web of Science, June, 2022). In contrast, over 10,000
articles are returned when queried for “Mechanical”. Recently, strong
correlations between corrosivity and surface roughness of metal AM
parts have been reported, indicating that characterization and control of
the surface texture is essential for part certification, service lifetime in a
given environment, and understanding of potential failure modes [6,
9–16]. However, consistent characterization of surface topographies
and textures encountered in metal AM parts remains a challenge due to
complex morphologies and a lack of standard measurement protocols
[17,18].
The surface texture and roughness quantify and differentiate surface
irregularities. Micro-roughness (0.8–10 µm) controls the reflectivity of
the sample, while the meso-roughness (10–80 µm) controls the bumpi­
ness [19–21]. However, others (including ISO 21920–2) have defined
“roughness” as only a composition of micro-roughness, while
meso-roughness is primarily due to waviness and lay (build pattern) [22,
23]. ISO 21920–2 defines the overall deviation from a plane as the
surface texture, or the combination of roughness, waviness, and lay. In
general, the surface texture can be equated to what others have defined
as a total roughness (nano, micro, meso, and macro roughness
* Correspondence to: Los Alamos National Laboratory, SM-30 Bikini Atoll Road, Los Alamos, NM 87545, USA.
E-mail address: Jamie.stull@lanl.gov (J.A. Stull).
https://doi.org/10.1016/j.addma.2023.103540
Received 28 September 2022; Received in revised form 4 March 2023; Accepted 2 April 2023
Available online 5 April 2023
2214-8604/© 2023 Published by Elsevier B.V.
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
combined) [19–21]. Meso-roughness (waviness and lay) surface features
have been demonstrated to correlate to other response measurements
such as corrosion [13]. A clearly defined inclusion set is critical for the
correlation to surface responses.
Surfaces from metal AM parts are highly irregular with pores, reentrant features, and agglomerates of partially melted feedstock parti­
cles (particularly in power bed fusion systems). These irregularities can
span up to two orders of magnitude (~0.1–50 µm) while also displaying
non-uniform optical properties due to local oxidation effects [23], and
present challenges for both contact and non-contact profilometry
measurements.
Roughness is often subject to many considerations, broadly including
the method of acquisition and analysis. Traditional roughness has been
characterized by the arithmetic mean height of the sample (Ra and Sa ),
the maximum z-range (Rz and Sz ), and the amount/shape of the peaks
and valleys of a surface. Balancing the resolution and the scan lengths
that need be achieved to fully characterize the topography is of signif­
icant importance to the results obtained. The advancement of optical
techniques to characterize the topography opens different possibilities
of rapid measurements with relative ease. However, the parameters of
acquisition can have an impact on the measurement, and subsequently
the roughness characterization.
Traditional roughness measurements and ISO 21920–3 and ASME
B49.1 standards are written with a focus on physical profilometry. Asbuilt metal AM parts are very rough (~30 µm – PBF-LB [24],
~200 µm – PBF-EM [25], ~600 µm – WAAM [26]) and can be subse­
quently processed to achieve a smoother finish (<1 µm). The 2.5 µm
radius of physical profilometers may inhibit measurement of small
features, such as what might be seen on polished (<1 µm) surfaces, and
of steep valleys, where the probe may not accurately measure, as the tip
may be too large to reach into the valleys. The standardization of
roughness measurements for optical profilometry will lead to improved
documentation and enhanced efficiency for the data density. The large
range of roughness exhibited by metal AM samples requires additional
consideration and caution.
Several studies have begun to investigate the relationship between
profilometry techniques and measured metal AM topographies. In 2016,
Townsend et al. performed a detailed review of relevant literature since
1997 on surface measurements of metal AM parts [17]. The sample data
is representative of a broad range of AM processes (e.g. PBF-LB, PBF-EB),
metal alloys, and test surface (e.g. truncheon, angled plates).
Stylus-based profilometry remained the most common technique (40 %
of examined literature), followed by focus variation (11 %) and confocal
microscopy (11 %). This is also reflected by the definition of ASME
B46.1 and ISO 21920–3 standards. While the authors discussed the
limitations of each technique, there is little quantitative evaluation of
the performance of topography measuring techniques. Whip et al.
compared the surface profile obtained from segmented 1D cross-sections
with the areal measurement obtained using structured white light
triangulation (SWLT) from parts printed using direct metal laser sin­
tering (PBF-LB). They concluded that the white light triangulation
approach was unable to accurately capture the depth of valley features
and thus underestimated the surface roughness [27]. Thompson et al.
compared confocal microscopy (CM), coherence scanning interferom­
etry (CSI), focus variation (FV) and X-ray computed tomography (XCT)
techniques on parts printed via laser based powder bed fusion (PBF-LB)
[23]. Due to a limited number of samples, the authors were unable to
definitively account for discrepancies between measured Sa , root mean
square height (Sq ), skewness (Ssk ), kurtosis (Sku ), and auto-correlation
length (Sal ) values. Cabanettes et al. compared the topography ob­
tained from an advanced coordinate measuring machine (CMM), contact
stylus measurements, FV, and segmented 1D cross-sections on PBF-LB
parts. They concluded that only FV was able to accurately capture
typical AM features compared to the CMM and contact stylus mea­
surements [28]. De Pastre et al. compared optical techniques (LSCM, FV,
and CSI) to XCT and CS for a polymer PBF system [29], concluding that
the optical techniques, including XCT, struggled in areas with a large
slope. They also found discrepancies between techniques are often of the
same magnitude as feature sizes. The polymer surfaces examined largely
stay in the medium Sa range of ~3–10 µm and minimize the added
complexity of reflectivity as compared to metal. All of these publications
explored as-printed parts known to be at the higher end of surface
roughness (~10 µm). Additionally, Jiang et al. reviewed feature-based
surface characterization, summarizing techniques and analytical ap­
proaches [30]. There also exist research into plane subtraction from
optical profilometry and the impacts of distortion of surface features on
such measurements [31]. Recent work in using power spectral density to
define new descriptors such as the surface periodicity index have also
worked to decouple process parameter impacts [32].
The correlation of surface roughness to the surface performance,
such as corrosion, of AM parts increases the importance of measuring the
surface roughness over a range of finishes. In this work we examine a
range of arithmetic mean heights (AMH) from < 1 µm to ~30 µm, an
encompassing range of expected surface finishes for metal AM parts
from as-printed to post-processed. In this study we aim to explicitly
define some impacts due to collection parameters and provide methods
for precise, comparable roughness measurements across techniques for
metal AM surfaces.
2. Materials and methods
2.1. Test specimens
Parallelopipeds were built using 15–45 µm diameter powder (d50 =
30 µm) 316LSS via PBF-LB on an EOS M290 to provide surfaces repre­
sentative of typical AM processes. The laser had a power of 214.2 W, a
scanning velocity of 928.1 mm/s and a volumetric energy density of
57.7 J/mm. There was hatch spacing of 100 µm and a layer thickness of
40 µm with a stripe scan pattern. A 0.00254 in contour scan is applied to
improve the as-built surface of the parts at 464 BTU/hr and 17.6 in/s. A
stripe scan pattern was used with skywriting for overhangs. The hatch
angle rotated 47◦ every layer and the gas flow was perpendicular to the
recoater and laser (vertical). Remaining PBF-LB print parameters are
listed in Table 1. The parallelopipeds had surfaces with build angles
0◦ (top), 45◦ (up skin), 90◦ (side), 135◦ (down skin) relative to the build
plate with dimensions of 18 mm (length) x 13 mm (width) x 13 mm
(height) (Fig. 1).
2.2. Topographical measurements
The surface topography of four randomly sampled parallelopipeds
from a single build plate were measured on four different instruments.
Three non-contact, areal, optical-based techniques were compared to a
contact stylus profilometer. Measurements were acquired from the
Table 1
PBF-LB printing parameters for the parallelopipeds in Fig. 1.
Hatch Offset Angle
Hatch Rotation Angle
Hatch Restriction Angle
Hatch Offset
Down Skin/Up Skin Ridge
Down Skin/Up Skin Overlap Stripes
Down Skin Laser Power
Down Skin Laser Speed
Down Skin Hatch Distance
Up Skin Laser Power
Up Skin Laser Speed
Up Skin/Infill Hatch Distance
Infill Exposure Mode
Infill Stripe Width
Infill Overlap Stripes
Infill Laser Power
Infill Laser Speed
2
0◦
47◦
30◦
1.18e-4 in
-0.0394 in
0 in
254 BTU/hr
37.4 in/s
0.00354 in
513 BTU/hr
20.3 in/s
0.00394 in
Single
0.472 in
0.00354 in
731 BTU/hr
36.5 in/s
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
to height, termed coherence scanning interferometry. Three magnifi­
cations were tested; 5.5x, 10x, and 20x. The manufacturer recom­
mended magnification for surface characterization is 10x. The
manufacturer listed z resolution is 0.15 nm.
2.2.4. Contact Stylus (CS)
The Bruker Dektak XT uses a diamond stylus with a 45◦ tip angle in
contact with the surface to track the height of the surface. The stylus is
dragged in a straight line over the surface. To collect a representative
topographical sample and to mitigate biasing due to the build of the
part, six total lines were collected with three parallel and three
perpendicular lines. Lines of length equivalent to the optical area scans
(0.8 mm, 2.5 mm, and 4 mm) were collected. A 2.5 µm radius B-type tip
was used with three mg force (29 mN) was found to be sufficient to
maintain contact with the surface, with no evidence of scratching of the
surface. The Bruker Dektak XT vertical resolution (by machine) is 8 nm
for a vertical range of 524 µm.
Fig. 1. Metal AM parallelopiped with four distinct skins, characterized by the
build angle relative to the build plate: 0◦ (top), 45◦ (up skin), 90◦ (side), and
135◦ (down skin) relative to the build plate. Part dimensions are 18 mm
(length) x 13 mm (width) x 13 mm (height). Red boxes indicate approximate
scan locations for both optical and physical profilometry measurements.
corner (Fig. 1) of each surface of the parallelopiped to standardize the
imaging area and reduce edge effects of the build. The measurements
were positioned so that a 2 mm boundary was maintained from the two
closest edges. Three different scan dimensions were measured for each
surface: 0.8 × 0.8 mm, 2.5 × 2.5 mm, and 4.0 × 4.0 mm. Images were
post-processed with a linear surface background subtraction unless
otherwise specified. Other background subtractions include a quadratic
surface subtraction on electrochemically polished samples, as well as
individual linear and quadratic surface subtraction of the pre-stitched
images. No other filters (S-filter, L-filter) were applied.
Details of the non-contact profilometers and the contact stylus
measurements are provided below and summarized in Table 2.
2.3. Electropolishing
Four parallelopipeds were electropolished to characterize the
topography of AM skins spanning a wide range of surface textures from
as-printed to mirror finish. The side skin (90◦ ) sharing a face with the
peg of the parallelopipeds was electropolished using EPS 2500 (Electro
Polish Systems), which is a propriety commercial solution comprised
primarily of sulfuric and phosphoric acid. A platinized titanium mesh
counter electrode was separated by a distance of 5 mm, such that the
current distribution is assumed to be uniform across the exposed face.
The operating temperature was set at 60 ◦ C. Electropolishing was per­
formed in a 14 L tank equipped with a 10 µm, 2.5′′ diameter, 4′′ long
polypropylene filter cartridge (Flo King Magnum Reusable Filter), which
was also used for agitation. The parts were electropolished using 6.5 A/
cm2 for times of 20, 60 and 90 min
2.2.1. Laser scanning confocal microscopy (LSCM)
The Keyence VK-X3000 combines traditional confocal microscopy
with focus variation. Three magnifications were tested: 5x, 10x, and
20x. A fourth specialty lens, 50x, was used to confirm optimal magni­
fication. The manufacturer recommended magnification for surface
characterization is 20x. A standard image on this instrument at 20x is
0.53 mm × 0.70 mm, therefore all image sizes selected in this study
comprise stitched images. The manufacturer listed z resolution is
0.01 nm.
3. Results and discussion
3.1. Build angle
Metal AM surfaces have a variety of surface topography and
morphology, largely dependent on print parameters and the build angle
of the skin [9]. A parallelopiped (Fig. 1) with build angles of 0◦ (top
skin), 45◦ (up skin), 90◦ (side skin), and 135◦ (down skin) was used as a
representative sample.
In Fig. 2, a 2.5 × 2.5 mm LSCM scan was visually compared for
different roughness and morphologies on the parallelopiped skins. The
AMH roughness, displayed in the top right of each image, is the average
distance from the mean height over the entire image (Sa ). The accu­
mulation of unmelted/partially melted particles on the down skin in
Fig. 2a increases the AMH. Gravity causes the drooping of the melt
pools, which balls up and interacts with unmelted particles, increasing
the roughness [33–35]. The up skin, opposite the down skin, contains
fewer particles on the surface (Fig. 2b). The top skin, with a single
exposed layer of the build, shows the laser melt pool tracks in 100 µm
periodic features and the fewest particles (Fig. 2c). Both skins agree with
the above conclusion that the melt pools and gravity appear to have a
large impact on roughness. The side skin roughness is related to the top
skin roughness due to the overlap of the laser path, where balling occurs
at the edges of melt pools (Fig. 2d-e) [35]. In order to characterize a
smoother surface, a side surface of a sample was DC electropolished for
90 min, to a mirror finish (Fig. 2f). At the same scale, the electropolished
surface exhibits minimal surface features compared to the as the
as-printed surfaces. The top (Fig. 2c) and side (Fig. 2d) skins show the
same AMH, within error, indicating that Sa or Ra does not uniquely
describe a surface texture by itself.
Four metal AM parallelopipeds were topographically analyzed by the
AMH (Sa and Ra ) in Fig. 3 as an initial metric across different build skins,
2.2.2. Structured white light triangulation (SWLT)
The Keyence VR-6000 uses structured white light triangulation,
where patterned white light distortion is imaged, and correlated to
height. Three magnifications were tested: 12x, 25x, and 80x. The
manufacturer recommended magnification for surface characterization
is 40x or above. The manufacturer listed z resolution is 400 nm.
2.2.3. Coherence scanning interferometry (CSI)
The Zygo Zegage Plus uses white light interference to create a known
pattern on the surface. The disturbance of the pattern is then correlated
Table 2
Lens parameters for different techniques. Marked (*) magnifications indicate the
manufacturer recommended magnification for roughness measurements.
Technique
Magnification
(X)
Working
Distance (mm)
Pixel Size (
μm)
Duplicates/
side
LSCM
LSCM
LSCM
SWLT
SWLT
SWLT
CSI
CSI
CSI
CS
5
10
20 *
12
25
80 *
5.5
10 *
20
-
22.5
16.5
3.1
76.2
76.2
76.2
8.0
7.4
4.7
0
2.75
1.4
0.7
24.0
12.0
4.0
1.46
0.81
0.41
0.14
4
4
4
4
4
4
4
4
4
24
3
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
Fig. 2. LSCM comparison of different parallelopiped skins: a) down b) up c) top d) side e) side (peg) f) polished side (peg). A 2.5 × 2.5 mm image was captured for
each skin with a 20x magnification lens and fully auto z-range.
LSCM. However, it was found to be highly dependent on the size of the
pixels (Fig. S6). Pixel size is highly dependent on the size of the scan due
to automatic data compression. The pixel size also correlates with the
longer focal distance that results in a 4 μm height accuracy, despite the
0.1 μm vertical resolution. The standard deviation increases at higher
roughness for SWLT. The large standard deviation across each skin and
each technique exemplifies the range of values reasonably achievable by
controlling acquisition parameters for these techniques. The Ra ,
measured by CS, also agrees fairly well with the measured Sa in Fig. 3,
but the deviation increases at larger AMH due to the lower amount of
data. CS has a slightly lower AMH for a side skin in Fig. 3, and in general,
which is likely due to the tip radii being larger than the optical resolu­
tion. Additionally, even with 6 total line scans, the amount of data from
CS is significantly less than the other techniques. This contributes to a
larger deviation. CS standard deviation is seen to increase as a function
of the surface roughness, leading to a large (~25 %) deviation for the
down skin.
Fig. 3. AMH variation across different parallelopiped skins and different
techniques (CS, CSI, LSCM, and SWLT). AMH values are averaged across four
samples, 3 magnifications, and 3 scan sizes. The as printed roughness mea­
surements were background subtracted with a linear surface, while the polished
samples were background subtracted with a quadratic surface.
3.2. Scan size
We translated the ISO 21920–3 (Table D.1) and ASME B46.1
(Table 3–2) standard Ra scan lengths to Sa areas. However, there are
instances where the section length (the required length to evaluate sta­
tistical values reliant on height of peaks and valleys) has been exchanged
with the evaluation length (the required length to evaluate statistical
values dependent on height as a function of distance) with consideration
of the experiment time [36]. The section lengths are defined as 1/5 the
evaluation length. Section lengths are only applicable for variables that
describe the peak height and valley/trough recessions, which does not
include some of the most commonly used values, Sa and Sq . We translate
the section lengths to areas, recognizing the increased amount of data
from area scans as compared to linear scans.
In Fig. 4 the influence of the translation of scan lengths to scan areas
is examined for the rough, as-printed side skins. The small 0.8×0.8 mm
scan area does not sufficiently capture representative surface details to
provide an adequate overall quantification of the surface. However, it
appears that a 2.5 × 2.5 mm scan is representative of the surfaces,
where Sa is calculated from 2D areal measurements collected via opticalbased profilometers, and Ra calculated from 1D linear measurements
collected with stylus profilometers. These are averaged across a range of
magnifications and scan sizes that are discussed throughout the paper,
simulating operating conditions broadly used in literature [9,17,23,29,
34,36].
Importantly, if the same area is examined, the optical measurements
of as-printed samples compare favorably using CSI and LSCM across all
skins (Fig. 3). However, the agreement with CSI only holds true when
the z-range is appropriately set. Using the AMH as a barometer, we show
in Fig. S5 that the z-range should be ~15 times greater than Sa in order
for the value to stabilize. This likely accounts for the tilt of the sample as
well as capturing the entire z-range of the measured area. The SWLT
profilometer is also generally within a standard deviation of CSI and
4
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
Fig. 4. Comparison of mean AMH (over 4 samples) on an as printed side skin as
a function of different scan sizes across the four different techniques (CS, CSI at
20X magnification, LSCM at 20X magnification, and SWLT at 40X magnifica­
tion). Samples were background subtracted with a linear surface.
Fig. 6. Sa as a function of magnification across optical techniques (LSCM, CSI,
and SWLT) on an as printed surface. The 50X magnification for LSCM only used
two samples (as compared to four samples for all other data points) due to
excessive time to collect the data. Each sample was background subtracted with
a linear surface.
evidenced by its subsequent agreement with the larger 4×4 mm scan
size roughness across LSCM, CSI and CS.
∫
1 lr
AMH =
|Z(x)|dx
(1)
lr 0
Above a magnification of 40x, the measured surface roughness stabi­
lizes. The lower Sa as compared to CSI and LSCM correlates with the
lower measured roughness in Fig. 3. CSI roughness plateaus at 10x
magnification, where a higher magnification does not add accuracy to
the surface roughness measurement. LSCM also performs similarly at
20x. However, at larger magnifications (>20x), the time to scan images
increases significantly such that it is not practical to complete mea­
surements at these magnifications. We also observe a lower resolution
and higher contrast at lower magnification in Fig. 7 and S3. Therefore,
we recommend using the lowest magnification possible, such that the
roughness and morphology are stable (Fig. 6).
To further quantify an appropriate scan size, we randomly sampled
pixels from a 4 × 4 mm scan and calculated the AMH using Eq. 1
(Fig. 5). The converged roughness agrees with the traditionally calcu­
lated roughness. Further, we can analyze the number of pixels (~103)
required for the roughness converge, indicating the resolution required
for a stable roughness measurement.
3.2.1. Magnification
Magnification has an effect on the morphology and roughness
detected of the surface. However, the practicality of large magnification
imaging need also be considered, as the time to measure an equivalent
area increases with increasing magnification. Sa as a function of tech­
nique and magnification is demonstrated in Fig. 6. Using the lenses
available (Table 2), we varied the magnification using the same scan
area of 2.5 mm × 2.5 mm. In Fig. 6, we show the impact of magnifica­
tion on the measured surface roughness. The SWLT roughness of the side
skin increases with increasing magnification as the resolution increases.
3.3. Post processing
In addition to acquisition parameters, post processing can also lead
to significantly different roughness values. If only micro-asperities are
considered as roughness, it is necessary to use waveform removal of
meso-asperities such as build tracks and waviness. There are studies
suggesting that corrosion occurs along the melt pool, indicating the
importance of meso-asperities surface texture contributions in metal AM
[13,33–35]. As such, the only leveling used in this study is dictated by
the overall geometry of the part. We apply a linear plane fit with no
background waveform removal for the as-built surfaces, as this might
remove build patterns that are important to capture. For the electro­
polished side skin, due to the preferential material removal at the edges
that causes rounding of the skin during electropolishing, a quadratic
plane fit was used. We particularly see the impact of background/tilt
removal in images that must be stitched together in order to acquire a
representative surface.
In Fig. 8 we examined the effect of background removal. A LSCM
stitched image of the electropolished side skin was processed in four
different ways; linear and quadratic plane fits over the entire stitched
image and of each individual image that make up the stitched image
Fig. 8a-d. Each image was processed to calculate the stochastic rough­
ness as a function of equivalent length in Fig. 8e, based on the number
and size of pixels used for each calculated roughness. These values were
compared to two AMH measurements from CS, each fitting ISO stan­
dards. The maroon dashed line in Fig. 8e represents six 0.8 mm scans
averaged together after a quadratic fit was applied to each equating to
4.8 mm, where three scans are perpendicular to the other three. The
grey dashed line is the average of three parallel and perpendicular 6 mm
scans to the waviness in Fig. 8b.
The impact of background removal on roughness values is summa
Fig. 5. Effect of scan length on measured stochastic roughness of a side skin on
the two most precise techniques. For comparison, the Ra from CS is shown (–).
The number of pixels is correlated to scan length by the dimension of a single
pixel in a given image. Each sample was collected at 20X magnification and
background subtracted with a linear surface.
5
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
Fig. 7. Comparison of 2.5 × 2.5 mm side skin across different magnifications using CSI: a) 5.5x b) 10x c) 20x. The white boxes highlight the same region of interest
across all acquired images. Each sample was background subtracted with a linear surface.
Fig. 8. Background form removal of a polished side skin by: a) linear surface removal, b) quadratic surface removal, c) linear surface removal of each stitched image,
and d) quadratic surface removal of each stitched image for an electropolished surface imaged by LSCM. The resulting stochastic roughness values are displayed in
(e). CS Ra values are displayed as dashed lines for two different scan lengths: 0.8 mm (maroon) and 4.0 mm (gray). The side skin was DC electropolished at 15 A for
60 min in EPS200.
rized in Fig. 8e, and visually represented in Fig. 8a-d. The Sa of quadratic
fitting of the polished surface (1.2μm) is 64 % lower than the Sa after a
linear plane fit (3.3μm), and the additional post processing of fitting
each individual image further reduced the roughness (0.38 and 0.19 μm
for individual linear and quadratic background removal respectively).
The leveling of each image (prior to stitching), rather than leveling the
entire stitched figure, removes mesoscale features from being included
in roughness calculations, significantly reducing the roughness of the
sample. This agrees with the CS 0.8 mm data (Fig. 8e, maroon dashed
line), where scans shorter than the wave period also show the same
AMH. The CS 4 mm scans (Fig. 8e, grey dashed line) are larger than a
period, and pick up the waviness, agreeing with the quadratic fit of the
overall image (Fig. 8e, black markers). This analysis was repeated for an
as-printed side skin in the supplementary information (Fig. S7). The
difference is noticeably muted in comparison due to the increased
number of particles on the surface and the overall scale of these domi­
nant surface features. However a noted 8 % decrease in the individually
fit data is demonstrated due to the underlying surface features being
removed.
making it complex to statistically quantify. However, the combination of
analyzing the morphology, in conjunction with roughness values gives
physical meaning to the statistical values.
We examined the morphology across optical techniques (Fig. 9) and
their ability to comparatively capture the surface at optimized condi­
tions established above. Images over the same areas, captured using
optimal imaging conditions (Table 3 and experimental Section 2.2),
were qualitatively compared on similar scales. The same feature across
all techniques was highlighted by a white box to help facilitate com­
parison. LSCM and CSI have the highest resolution of these images and
captures the highest detail in the morphology. In contrast, SWLT shows
lower resolution, possibly due to its significantly longer working
distance.
3.4.1. Morphology analysis
AMH could not solely differentiate the different skins, therefore,
additional parameters were examined to try and distinguish between the
morphologies observed for the different skins for a given build angle
(Fig. 2c-d). Another commonly used metric, the maximum height dif­
ference (MHD), Sz or Rz , can be an additional indication of how rough a
sample is separate from AMH. In Fig. 10, we examined MHD across the
different skins. We averaged these vales across the four different samples
after background removal.
In Fig. 10, the side skin has the smallest Sz values, in contrast with the
top skin, which has the smallest AMH. The down skin, with the increased
3.4. Morphology
The use of Sa does not tell the entire story, as there are multiple
morphologies that can lead to identical Sa (Fig. 2c vs 2d). Multiple
statistical parameters will be required to fully characterize surfaces,
6
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Additive Manufacturing 69 (2023) 103540
Fig. 9. Comparison of the impact of different techniques on the surface texture. a) SWLT b) CSI c) LSCM. A white dashed box highlights the same feature across all
techniques. Each sample was background subtracted with a linear surface.
Supplementary information).
In this work, it is difficult to use skewness and kurtosis to conclu­
sively characterize these AM skins due to weak agreement between
techniques, with large standard deviations. The slightly (< 1) positive
skewness values in Fig. S3 suggest there are more peaks than there are
valleys/pores. A lower kurtosis (< 3) (Fig. S4) indicates that the height is
broadly distributed, suggesting the majority of the peaks are more wavy,
rather than sharp.
In order to differentiate between micro- and meso-asperities, one
would think that comparison of peak density (Spd ) would be useful.
However, the definition of a peak, between instruments and differences
in how background filtering is applied, can vastly impact the peak
density. In order to standardize the measurements, using a ratio of the
optically measured surface area, compared to the geometric surface
(SO/G ) provides a useful metric. The optical surface area includes
topography accessible to optical measurements. With an increasing
amount of surface features, the optical surface area increases. AMH is
not necessarily reflective of different optical surface areas, as shown by
the equivalent values in Fig. 2c-d. This is also reflected in Fig. 3, where
there are three surfaces (top, up, and side skins) that are close in AMH
values. In Fig. 11, the normalized surface area (SO/G) of each skin was
averaged across four samples for a scan area of 2.5 mm × 2.5 mm. An
additional side skin that was electropolished for 60 min to a smooth
finish was also examined. A polished surface minimizes the peaks,
moving SO/G towards the optimal value of SO/G = 1.
The normalized surface area reflects different morphologies in
Fig. 11. SO/G captures the micro-asperities with the increasing optical
Table 3
Summary of optimized acquisition parameters.
Technique
Size
Magnification
Z-Range
CS
SWLT
CSI
LSCM
12 mm
2.5 mm × 2.5 mm
2.5 mm × 2.5 mm
2.5 mm × 2.5 mm
2.5 µm
80X
10X
20X
N/A
Auto
15*Sa
Auto
Fig. 10. Comparison of mean maximum Z range, Sz , across all skins of the
parallelopiped for 2.5 mm × 2.5 mm area. Measurements for all four tech­
niques (CS, CSI, LSCM, and SWLT) are compared. Four samples of each skin are
averaged for every technique. The as printed roughness measurements were
background subtracted with a linear surface, while the polished samples were
background subtracted with a quadratic surface.
amount of partially unmelted particles, has a large instrument-toinstrument variation, potentially due to many crevices/valleys be­
tween particles. Comparing different techniques, the CSI (Fig. 10, green
triangles), which uses interferometry, has a large deviation and is
imprecise in comparison to the other techniques. The physical profil­
ometry is consistently the lowest MHD value across the techniques used.
It is probable that the radius of curvature of the probe prevents the probe
from reaching the full depth of narrow valleys.
The size, shape, and distribution of peaks and valleys can be used to
uniquely identify surfaces, even when they have indistinguishable
arithmetic mean height. The maximum height difference (Sz ) can be
used to differentiate the size of the peaks at the extremes. Provided the
peaks are relatively uniform, or the smaller peaks are, at minimum,
more frequent than their larger counterparts, the peak density can also
be used to differentiate the size of the surface in addition to the distri­
bution of peaks of a surface. Skewness (Ssk ) differentiates between a
dominance between peaks (>0) and valleys (<0), while the kurtosis
(Sku ) differentiates between the “sharpness” of the peak (see
Fig. 11. The normalized mean surface areas, the ratio of optical surface area to
geometric area, is compared over a 2.5 mm × 2.5 mm scan area. All four
measurement techniques (CS, CSI, LSCM, and SWLT) are compared over four
samples for every skin. The as printed roughness measurements were back­
ground subtracted with a linear surface, while the polished samples were
background subtracted with a quadratic surface.
7
A. Mirabal et al.
Additive Manufacturing 69 (2023) 103540
surface area. Lower build angles correlate to lower SO/G , meaning a
lower number of micro-asperities. SWLT and CS do not capture the same
change in surface area due to the lower resolution. While the CS tip
radius is larger (2.5 µm) than the pixel size (1.8 µm for a
2.5 mm × 2.5 mm scan), the resolution of CS is also limited by the depth
and width of the valleys in comparison to the probe, where a probe size
and angle of 45◦ limits the angle of a valley that can be measured.
A summary of results is presented in Table 3, where the roughness
measurements are compared across all techniques. The parameters are
summarized for each instrument in Table 2. A linear plane fit with no
background waveform removal was utilized for all as-built surfaces,
since other waveform removal might eliminate build patterns that are
important components of the skin. A quadratic plane fit was used for the
electropolished side surface, due to the preferential material removal at
the edges. The optimized parameters are discussed throughout the text
and summarized in Table 3, with the resulting surface roughness pre­
sented graphically in Fig. 12.
Optimizing the image acquisition parameters increased the precision
and agreement AMH for the techniques, particularly between LSCM and
CSI, illustrated in Fig. 12. SWLT appears to trend towards more mild
values, having higher relative AMH values at small AMH absolute values
and conversely having lower relative AMH at larger absolute values. The
deviation of CS is still relatively large due to the smaller equivalent
distance. The comparison of top skin AMH values in Figs. 3 and 12 show
a statistically relevant decreased average value in the optimized imaging
in Fig. 12. This decrease in values from optimization of scan parameters
can alter the interpretation of skin-to-skin comparisons.
Fig. 12. Mean of four AMH measurements across different parallelopiped build
angles and different techniques (CS, CSI, LSCM, and SWLT) under ideal imaging
parameters. The as printed roughness measurements were background sub­
tracted with a linear surface, while a quadratic surface was background sub­
tracted from the polished samples.
Funding
This work was supported by the US Department of Energy, USA
through the Los Alamos National Laboratory. Los Alamos National
Laboratory is operated by Triad National Security, LLC, for the National
Nuclear Security Administration of U.S Department of Energy (Contract
No.89233218CNA000001). This research was supported by the LANL
Office of Engineering and Technology Maturation Work, USA was per­
formed, in part, at the Center for Integrated Nanotechnologies, an Office
of Science User Facility operated for the U.S. Department of Energy
(DOE) Office of Science.
4. Conclusions
In this work, the most common value for describing surface rough­
ness, Sa , found to provide a broad description of the average surface, but
ineffective for differentiating the different types of metal AM surface
features. AMH, which is used to define Sa and Ra is a useful descriptor in
conjunction with other values. The ratio of total to geometric areas was
found to distinguish between all examined skins. While kurtosis and
skewness have the potential to more completely describe these surfaces,
they were found to be not particularly useful in this study. For CSI, it was
shown that using a Z-range approximately 15 times larger than Sa was
necessary, in in order to avoid impacts of tilt on surface roughness.
Different statistical values (Sz , Ssk , Sku , and SO/G ) were examined to
describe the morphology in combination with Sa because multiple skins
were shown to have similar Sa values.
Of the four compared techniques, LSCM was least impacted by the
large range of surface textures, including a mirror finish. When
considering other texture parameters, such as the maximum height, Sz ,
we see limitations from CSI for metal AM surfaces. CSI did not have the
accuracy or precision in the Z-range to compare with other techniques.
Magnification is a key parameter, where manufacturer recommenda­
tions provided the best balance of resolution to image time. Background
removal impacts surface roughness characterization, with a demon­
strated order of magnitude difference in AMH, depending on the back­
ground removal method. The use of randomly sampled height values
from within a dataset defined the minimum equivalent distance required
for precise measurement of surface roughness variables. This method is
transferrable for defining the minimum amount of data required for
accurate, representative roughness measurement across a wide range of
surface roughness. Measurement of surface roughness is essential to the
interpretation of wear, fatigue, aging, and other aspects of additively
manufactured components. This study demonstrates precise roughness
measurements techniques for additively manufactured surfaces, mini­
mizing measurement discrepancies and enabling comparison between
different specific measurement techniques through rational choice of
acquisition parameters.
CRediT authorship contribution statement
Ilker Loza-Hernandez: Methodology, Investigation, Formal anal­
ysis, Data curation. Courtney Clark: Writing – review & editing,
Writing – original draft, Conceptualization. Daniel E. Hooks: Writing –
review & editing, Resources, Investigation, Formal analysis. Michael
McBride: Writing – review & editing, Writing – original draft,
Conceptualization. Jamie A. Stull: Writing – review & editing, Valida­
tion, Supervision, Resources, Project administration, Methodology,
Investigation, Funding acquisition.
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.
Data Availability
Data will be made available on request.
Acknowledgments
We are grateful towards Colt Montgomery, Robin Pacheco and
Michael Brand for their help in building the laser powder bed stainless
steel parallelopipeds.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.addma.2023.103540.
8
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Additive Manufacturing 69 (2023) 103540
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