Abstract

Laser powder bed fusion (L-PBF) parts often require post-processing prior to use in engineering applications to improve mechanical properties and modify the as-built surface topography. The ability to tune the L-PBF process parameters to obtain designer as-built surface topography could reduce the need for post-processing. However, the relationship between the as-built surface topography and the L-PBF process parameters is currently not well-understood. In this paper, we derive data-driven models from surface topography data and L-PBF process parameters using machine learning (ML) algorithms. The prediction accuracy of the data-driven models derived from ML algorithms exceeds that of the multivariate regression benchmark because the latter does not always capture the complex relationship between the as-built surface topography parameters and the corresponding L-PBF process parameters in a single best-fit equation. Data-driven models based on decision tree (interpretable) and artificial neural network (non-interpretable) algorithms display the highest prediction accuracy. We also show experimental evidence that thermocapillary convection and melt track overlap are important drivers of the formation of as-built surface topography.

References

1.
Gibson
,
I.
,
Rosen
,
D.
, and
Stucker
,
B.
,
2015
,
Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing
,
Springer US
,
New York, NY
.
2.
Kruth
,
J.-C.
,
Leu
,
M. C.
, and
Nakagawa
,
T.
,
1998
, “
Progress in Additive Manufacturing and Rapid Prototyping
,”
Ann. CIRP
47
(
2
), pp.
525
540
.
3.
Singh
,
S.
, and
Ramakrishna
,
S.
,
2017
, “
Biomedical Applications of Additive Manufacturing: Present and Future
,”
Curr. Opin. Biomed. Eng.
,
2
(
6
), pp.
105
115
.
4.
Rochus
,
P.
,
Plesseria
,
J.
,
van Elsen
,
M.
,
Kruth
,
J.
,
Carrus
,
R.
, and
Dormal
,
T.
,
2007
, “
New Applications of Rapid Prototyping and Rapid Manufacturing (RP/RM) Technologies for Space Instrumentation
,”
Acta Astronaut.
,
61
(
1–6
), pp.
352
359
.
5.
Martin
,
J.
,
Yahata
,
B.
,
Hundley
,
J.
,
Mayer
,
J.
,
Schaedler
,
T.
, and
Pollock
,
T.
,
2017
, “
3D Printing of High-Strength Aluminium Alloys
,”
Nature
,
549
(
7672
), pp.
365
369
.
6.
Frazier
,
W.
,
2014
, “
Metal Additive Manufacturing: A Review
,”
J. Mater. Eng. Perform.
,
23
(
6
), pp.
1917
1928
.
7.
Goel
,
S.
,
Neikter
,
M.
,
Capek
,
J.
,
Polatidis
,
E.
,
Colliander
,
M. H.
,
Joshi
,
S.
, and
Pederson
,
R.
,
2020
, “
Residual Stress Determination by Neutron Diffraction in Powder Bed Fusion-Built Alloy 718: Influence of Process Parameters and Post-Treatment
,”
Mater. Des.
,
195
(
10
), p.
109045
.
8.
Aversa
,
A.
,
Lorusso
,
M.
,
Trevisan
,
F.
,
Ambrosio
,
E. P.
,
Calignano
,
F.
,
Manfredi
,
D.
,
Biamino
,
S.
,
Fino
,
P.
,
Lombardi
,
M.
, and
Pavese
,
M.
,
2017
, “
Effect of Process and Post-Process Conditions on the Mechanical Properties of an A357 Alloy Produced via Laser Powder Bed Fusion
,”
Metals (Basel)
,
7
(
2
), pp.
1
9
.
9.
Lou
,
S.
,
Jiang
,
X.
,
Sun
,
W.
,
Zeng
,
W.
,
Pagani
,
L.
, and
Scott
,
P. J.
,
2019
, “
Characterisation Methods for Powder Bed Fusion Processed Surface Topography
,”
Precis. Eng.
,
57
, pp.
1
15
.
10.
Kumbhar
,
N.
, and
Mulay
,
A.
,
2018
, “
Post Processing Methods Used to Improve Surface Finish of Products Which Are Manufactured by Additive Manufacturing Technologies: A Review
,”
J. Inst. Eng. (India): Ser., C
,
99
(
4
), pp.
481
487
.
11.
Jiang
,
R.
,
Mostafaei
,
A.
,
Pauza
,
J.
,
Kantzos
,
C.
, and
Rollett
,
A. D.
,
2019
, “
Varied Heat Treatments and Properties of Laser Powder Bed Printed Inconel 718
,”
Mater. Sci. Eng.: A
,
755
(
5
), pp.
170
180
.
12.
Carter
,
L.
,
Martin
,
C.
,
Withers
,
P.
, and
Attallah
,
M.
,
2014
, “
The Influence of the Laser Scan Strategy on Grain Structure and Cracking Behaviour in SLM Powder-Bed Fabricated Nickel Superalloy
,”
J. Alloys Compd.
,
615
(
12
), pp.
338
347
.
13.
Newell
,
D. J.
,
O’Hara
,
R. P.
,
Cobb
,
G. R.
,
Palazotto
,
A. N.
,
Kirka
,
M. M.
,
Burggraf
,
L. W.
, and
Hess
,
J. A.
,
2019
, “
Mitigation of Scan Strategy Effects and Material Anisotropy Through Supersolvus Annealing in LPBF IN718
,”
Mater. Sci. Eng.: A
,
764
(
August
), p.
138230
.
14.
Pyka
,
G.
,
Kerckhofs
,
G.
,
Papantoniou
,
I.
,
Speirs
,
M.
,
Schrooten
,
J.
, and
Wevers
,
M.
,
2013
, “
Surface Roughness and Morphology Customization of Additive Manufactured Open Porous Ti6Al4V Structures
,”
Materials
,
6
(
10
), pp.
4737
4757
.
15.
Mohammadian
,
N.
,
Turenne
,
S.
, and
Brailovski
,
V.
,
2018
, “
Surface Finish Control of Additively-Manufactured Inconel 625 Components Using Combined Chemical-Abrasive Flow Polishing
,”
J. Mater. Process. Technol.
,
252
, pp.
728
738
.
16.
Calignano
,
F.
,
Manfredi
,
D.
,
Ambrosio
,
E.
,
Iuliano
,
L.
, and
Fino
,
P.
,
2013
, “
Influence of Process Parameters on Surface Roughness of Aluminum Parts Produced by DMLS
,”
Int. J. Adv. Manuf. Technol.
,
67
(
9–12
), pp.
2743
2751
.
17.
Lesyk
,
D. A.
,
Martinez
,
S.
,
Mordyuk
,
B. N.
,
Dzhemelinskyi
,
V. V.
,
Lamikiz
,
A.
, and
Prokopenko
,
G.
,
2020
, “
Post-processing of the Inconel 718 Alloy Parts Fabricated by Selective Laser Melting: Effects of Mechanical Surface Treatments on Surface Topography, Porosity, Hardness and Residual Stress
,”
Surf. Coat. Technol.
,
381
(
1
), p.
125136
.
18.
Eidt
,
W.
,
Tatman
,
E.
,
McCarther
,
J.
,
Kastner
,
J.
,
Gunther
,
S.
, and
Gockel
,
J.
,
2019
, “
Surface Roughness Characterization in Laser Powder Bed Fusion Additive Manufacturing
,”
Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium—An Additive Manufacturing Conference, SFF 2019
,
Austin, TX
,
Aug. 12–14
, pp.
2165
2176
.
19.
Triantaphyllou
,
A.
,
Giusca
,
C. L.
,
Macaulay
,
G. D.
,
Roerig
,
F.
,
Hoebel
,
M.
,
Leach
,
R. K.
,
Tomita
,
B.
, and
Milne
,
K. A.
,
2015
, “
Surface Texture Measurement for Additive Manufacturing
,”
Surf. Topogr. Metrol. Prop.
,
3
(
2
),
024002
.
20.
Galati
,
M.
,
Rizza
,
G.
,
Defanti
,
S.
, and
Denti
,
L.
,
2021
, “
Surface Roughness Prediction Model for Electron Beam Melting (EBM) Processing Ti6Al4V
,”
Precis. Eng.
,
69
(
September 2020
), pp.
19
28
.
21.
Charles
,
A.
,
Elkaseer
,
A.
,
Thijs
,
L.
,
Hagenmeyer
,
V.
, and
Scholz
,
S.
,
2019
, “
Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting
,”
Appl. Sci.
,
9
(
6
), p.
1256
.
22.
Fox
,
J. C.
,
Moylan
,
S. P.
, and
Lane
,
B. M.
,
2016
, “
Effect of Process Parameters on the Surface Roughness of Overhanging Structures in Laser Powder Bed Fusion Additive Manufacturing
,”
Proc. CIRP
45
(
1
), pp.
131
134
.
23.
Whip
,
B.
,
Sheridan
,
L.
, and
Gockel
,
J.
,
2019
, “
The Effect of Primary Processing Parameters on Surface Roughness in Laser Powder Bed Additive Manufacturing
,”
Int. J. Adv. Manuf. Technol.
,
103
(
9–12
), pp.
4411
4422
.
24.
Detwiler
,
S.
,
Watring
,
D.
,
Spear
,
A.
, and
Raeymaekers
,
B.
,
2021
, “
Relating the Surface Topography of As-Built Inconel 718 Surfaces to Laser Powder Bed Fusion Process Parameters Using Multivariate Regression Analysis
,”
Precis. Eng.
,
74
(
December 2021
), pp.
303
315
.
25.
Khorasani
,
A. M.
,
Gibson
,
I.
,
Ghasemi
,
A.
, and
Ghaderi
,
A.
,
2020
, “
Modelling of Laser Powder Bed Fusion Process and Analysing the Effective Parameters on Surface Characteristics of Ti-6Al-4V
,”
Int. J. Mech. Sci.
,
168
, p.
105299
.
26.
Cao
,
L.
,
Li
,
J.
,
Hu
,
J.
,
Liu
,
H.
,
Wu
,
Y.
, and
Zhou
,
Q.
,
2021
, “
Optimization of Surface Roughness and Dimensional Accuracy in LPBF Additive Manufacturing
,”
Opt. Laser Technol.
,
142
, p.
107246
.
27.
Özel
,
T.
,
Altay
,
A.
,
Kaftanoğlu
,
B.
,
Leach
,
R.
,
Senin
,
N.
, and
Donmez
,
A.
,
2020
, “
Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion
,”
ASME J. Manuf. Sci. Eng.
,
142
(
1
), p.
011008
.
28.
Meng
,
L.
,
McWilliams
,
B.
,
Jarosinski
,
W.
,
Park
,
H.
,
Jung
,
Y.
,
Lee
,
J.
, and
Zhang
,
J.
,
2020
, “
Machine Learning in Additive Manufacturing: A Review
,”
JOM
,
72
(
6
), pp.
2363
2377
.
29.
Sen
,
A.
, and
Srivastava
,
M.
,
1990
,
Regression Analysis: Theory, Methods, and Applications
,
Springer-Verlag
,
New York
.
30.
Mate
,
C. M.
,
2008
,
Tribology on the Small Scale: A Bottom up Approach to Friction, Lubrication, and Wear
,
Oxford University Press
,
New York
.
31.
Kalin
,
M.
,
Pogačnik
,
A.
,
Etsion
,
I.
, and
Raeymaekers
,
B.
,
2016
, “
Comparing Surface Topography Parameters of Rough Surfaces Obtained With Spectral Moments and Deterministic Methods
,”
Tribol. Int.
,
93
(
1
), pp.
137
141
.
32.
Pawar
,
G.
,
Pawlus
,
P.
,
Etsion
,
I.
, and
Raeymaekers
,
B.
,
2013
, “
The Effect of Determining Topography Parameters on Analyzing Elastic Contact Between Isotropic Rough Surfaces
,”
ASME J. Tribol.
,
135
(
1
), p.
011401
.
33.
ASTM F1877, Standard Practice for Characterization of Particles, Standard, 2016
.”.
34.
ASTM E466-07, Standard Practice for Conducting Force Controlled Constant Amplitude Axial Fatigue Tests of Metallic Materials, Standard, 2007
.”.
35.
Chen
,
Z.
,
Wu
,
X.
,
Tomus
,
D.
, and
Davies
,
C.
,
2018
, “
Surface Roughness of Selective Laser Melted Ti-6Al-4V Alloy Components
,”
Addit. Manuf.
,
21
, pp.
91
103
.
36.
Watring
,
D.
,
Benzing
,
J.
,
Hrabe
,
N.
, and
Spear
,
A.
,
2020
, “
Effects of Laser-Energy Density and Build Orientation on the Structure–Property Relationships in as-Built Inconel 718 Manufactured by Laser Powder Bed Fusion
,”
Addit. Manuf.
,
36
, p.
101425
.
37.
Watring
,
D. S.
,
Carter
,
K. C.
,
Crouse
,
D.
,
Raeymaekers
,
B.
, and
Spear
,
A. D.
,
2019
, “
Mechanisms Driving High-Cycle Fatigue Life of As-Built Inconel 718 Processed by Laser Powder Bed Fusion
,”
Mater. Sci. Eng.: A
,
761
p.
137993
.
38.
Townsend
,
A.
,
Senin
,
N.
,
Blunt
,
L.
,
Leach
,
R. K.
, and
Taylor
,
J. S.
,
2016
, “
Surface Texture Metrology for Metal Additive Manufacturing: A Review
,”
Precis. Eng.
(
10
),
46
, pp.
34
47
.
39.
ISO 25178-2 2012 ‘Geometrical Product Specification (GPS)—Surface Texture: Areal—Part 2: Terms, Definitions and Surface Texture Parameters
.’”
40.
McCool
,
J.
,
1987
, “
Relating Profile Instrument Measurements to the Functional Performance of Rough Surfaces
,”
ASME J. Tribol.
,
109
(
2
), pp.
264
270
.
41.
Rebala
,
G.
,
Ravi
,
A.
, and
Churiwala
,
S.
,
2019
,
An Introduction to Machine Learning
,
Springer
,
New York
.
42.
Orr
,
M.
,
1996
,
“Introduction to Radial Basis Function Networks,” Center for Cognitive Science
,
University of Edinburgh
,
Edingburgh, UK
.
43.
Liu
,
D. C.
, and
Nocedal
,
J.
,
1989
, “
On the Limited Memory BFGS Method for Large Scale Optimization
,”
Math. Progr.
,
45
(
1–3
), pp.
503
528
.
44.
Osisanwo
,
F. Y.
,
Akinsola
,
J. E. T.
,
Awodele
,
O.
,
Hinmikaiye
,
J. O.
,
Olakanmi
,
O.
, and
Akinjobi
,
J.
,
2017
, “
Supervised Machine Learning Algorithms: Classification and Comparison
,”
Int. J. Comput. Trends Technol.
,
48
(
3
), pp.
128
138
.
45.
Yang
,
Y.
,
Großmann
,
A.
,
Kühn
,
P.
,
Mölleney
,
J.
,
Kropholler
,
L.
,
Mittelstedt
,
C.
, and
Xu
,
B.-X.
,
2022
, “
Validated Dimensionless Scaling Law for Melt Pool Width in Laser Powder Bed Fusion
,”
J. Mater. Process. Technol.
,
299
(
1
), p.
117316
.
46.
Zhao
,
X.
,
Xu
,
S.
, and
Liu
,
J.
,
2017
, “
Surface Tension of Liquid Metal: Role, Mechanism and Application
,”
Front. Energy
,
11
(
4
), pp.
535
567
.
47.
Wei
,
P.
,
Liu
,
H.
, and
Lin
,
C.
,
2012
, “
Scaling Weld or Melt Pool Shape Induced by Thermocapillary Convection
,”
Int. J. Heat Mass Transfer
,
55
(
9–10
), pp.
2328
2337
.
You do not currently have access to this content.