Abstract

Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine-learning (ML) model to predict the melt-pool size during the scanning of a multitrack build. To account for the effect of thermal history on melt-pool size, a so-called (prescan) initial temperature is predicted at the lower-level of the modeling architecture and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the autodesk'snetfabbsimulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance, and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.

References

1.
Levy
,
G. N.
,
Schindel
,
R.
, and
Kruth
,
J.-P.
,
2003
, “
Rapid Manufacturing and Rapid Tooling With Layer Manufacturing (LM) Technologies, State of the Art and Future Perspectives
,”
CIRP Ann.
,
52
(
2
), pp.
589
609
.10.1016/S0007-8506(07)60206-6
2.
Kruth
,
J.-P.
,
Levy
,
G.
,
Klocke
,
F.
, and
Childs
,
T.
,
2007
, “
Consolidation Phenomena in Laser and Powder-Bed Based Layered Manufacturing
,”
CIRP Ann.
,
56
(
2
), pp.
730
759
.10.1016/j.cirp.2007.10.004
3.
Gouge
,
M.
, and
Michaleris
, and
P.
, eds.,
2017
,
Thermo-Mechanical Modeling of Additive Manufacturing
,
Butterworth-Heinemann
, Oxford, UK.
4.
Dilip
,
J.
,
Zhang
,
S.
,
Teng
,
C.
,
Zeng
,
K.
,
Robinson
,
C.
,
Pal
,
D.
, and
Stucker
,
B.
,
2017
, “
Influence of Processing Parameters on the Evolution of Melt Pool, Porosity, and Microstructures in Ti-6Al-4V Alloy Parts Fabricated by Selective Laser Melting
,”
Prog. Addit. Manuf.
,
2
(
3
), pp.
157
167
.10.1007/s40964-017-0030-2
5.
Kumar
,
P.
,
Farah
,
J.
,
Akram
,
J.
,
Teng
,
C.
,
Ginn
,
J.
, and
Misra
,
M.
,
2019
, “
Influence of Laser Processing Parameters on Porosity in Inconel 718 During Additive Manufacturing
,”
Int. J. Adv. Manuf. Technol.
,
103
(
1–4
), pp.
1497
1507
.10.1007/s00170-019-03655-9
6.
Ning
,
J.
,
Sievers
,
D. E.
,
Garmestani
,
H.
, and
Liang
,
S. Y.
,
2020
, “
Analytical Modeling of Part Porosity in Metal Additive Manufacturing
,”
Int. J. Mech. Sci.
,
172
, p.
105428
.10.1016/j.ijmecsci.2020.105428
7.
King
,
W. E.
,
Anderson
,
A. T.
,
Ferencz
,
R. M.
,
Hodge
,
N. E.
,
Kamath
,
C.
,
Khairallah
,
S. A.
, and
Rubenchik
,
A. M.
,
2015
, “
Laser Powder Bed Fusion Additive Manufacturing of Metals; Physics, Computational, and Materials Challenges
,”
Appl. Phys. Rev.
,
2
(
4
), p.
041304
.10.1063/1.4937809
8.
King
,
W.
,
Anderson
,
A. T.
,
Ferencz
,
R. M.
,
Hodge
,
N. E.
,
Kamath
,
C.
, and
Khairallah
,
S. A.
,
2015
, “
Overview of Modeling and Simulation of Metal Powder Bed Fusion Process at Lawrence Livermore National Laboratory
,”
Mater. Sci. Technol.
,
31
(
8
), pp.
957
968
.10.1179/1743284714Y.0000000728
9.
Razvi
,
S. S.
,
Feng
,
S.
,
Narayanan
,
A.
,
Lee
,
Y.-T. T.
, and
Witherell
,
P.
,
2019
, “
A Review of Machine Learning Applications in Additive Manufacturing
,”
ASME
Paper No. DETC2019-98415.10.1115/DETC2019-98415
10.
Meng
,
L.
,
McWilliams
,
B.
,
Jarosinski
,
W.
,
Park
,
H.-Y.
,
Jung
,
Y.-G.
,
Lee
,
J.
, and
Zhang
,
J.
,
2020
, “
Machine Learning in Additive Manufacturing: A Review
,”
JOM
,
72
(
6
), pp.
2363
2377
.10.1007/s11837-020-04155-y
11.
Gobert
,
C.
,
Reutzel
,
E. W.
,
Petrich
,
J.
,
Nassar
,
A. R.
, and
Phoha
,
S.
,
2018
, “
Application of Supervised Machine Learning for Defect Detection During Metallic Powder Bed Fusion Additive Manufacturing Using High Resolution Imaging
,”
Addit. Manuf.
,
21
, pp.
517
528
.10.1016/j.addma.2018.04.005
12.
Yuan
,
B.
,
Guss
,
G. M.
,
Wilson
,
A. C.
,
Hau-Riege
,
S. P.
,
DePond
,
P. J.
,
McMains
,
S.
,
Matthews
,
M. J.
, and
Giera
,
B.
,
2018
, “
Machine-Learning-Based Monitoring of Laser Powder Bed Fusion
,”
Adv. Mater. Technol.
,
3
(
12
), p.
1800136
.10.1002/admt.201800136
13.
Scime
,
L.
, and
Beuth
,
J.
,
2018
, “
A Multi-Scale Convolutional Neural Network for Autonomous Anomaly Detection and Classification in a Laser Powder Bed Fusion Additive Manufacturing Process
,”
Addit. Manuf.
,
24
, pp.
273
286
.10.1016/j.addma.2018.09.034
14.
Scime
,
L.
, and
Beuth
,
J.
,
2019
, “
Using Machine Learning to Identify In-Situ Melt Pool Signatures Indicative of Flaw Formation in a Laser Powder Bed Fusion Additive Manufacturing Process
,”
Addit. Manuf.
,
25
, pp.
151
165
.10.1016/j.addma.2018.11.010
15.
Aminzadeh
,
M.
, and
Kurfess
,
T. R.
,
2019
, “
Online Quality Inspection Using Bayesian Classification in Powder-Bed Additive Manufacturing From High-Resolution Visual Camera Images
,”
J. Intell. Manuf.
,
30
(
6
), pp.
2505
2523
.10.1007/s10845-018-1412-0
16.
Lu
,
Z.
,
Li
,
D.
,
Lu
,
B.
,
Zhang
,
A.
,
Zhu
,
G.
, and
Pi
,
G.
,
2010
, “
The Prediction of the Building Precision in the Laser Engineered Net Shaping Process Using Advanced Networks
,”
Opt. Lasers Eng.
,
48
(
5
), pp.
519
525
.10.1016/j.optlaseng.2010.01.002
17.
Mozaffar
,
M.
,
Paul
,
A.
,
Al-Bahrani
,
R.
,
Wolff
,
S.
,
Choudhary
,
A.
,
Agrawal
,
A.
,
Ehmann
,
K.
, and
Cao
,
J.
,
2018
, “
Data-Driven Prediction of the High-Dimensional Thermal History in Directed Energy Deposition Processes Via Recurrent Neural Networks
,”
Manuf. Lett.
,
18
, pp.
35
39
.10.1016/j.mfglet.2018.10.002
18.
Ren
,
K.
,
Chew
,
Y.
,
Zhang
,
Y.
,
Fuh
,
J.
, and
Bi
,
G.
,
2020
, “
Thermal Field Prediction for Laser Scanning Paths in Laser Aided Additive Manufacturing by Physics-Based Machine Learning
,”
Comput. Methods Appl. Mech. Eng.
,
362
, p.
112734
.10.1016/j.cma.2019.112734
19.
Rong-Ji
,
W.
,
Xin-Hua
,
L.
,
Qing-Ding
,
W.
, and
Lingling
,
W.
,
2009
, “
Optimizing Process Parameters for Selective Laser Sintering Based on Neural Network and Genetic Algorithm
,”
Int. J. Adv. Manuf. Technol.
,
42
(
11–12
), pp.
1035
1042
.10.1007/s00170-008-1669-0
20.
Zhang
,
W.
,
Mehta
,
A.
,
Desai
,
P. S.
, and
Higgs
,
C.
,
2017
, “
Machine Learning Enabled Powder Spreading Process Map for Metal Additive Manufacturing (AM)
,”
International Solid Freeform Fabrication Symposium
,
Austin, TX
, pp.
1235
1249
.
21.
Yang
,
Z.
,
Eddy
,
D.
,
Krishnamurty
,
S.
,
Grosse
,
I.
,
Denno
,
P.
,
Witherell
,
P. W.
, and
Lopez
,
F.
,
2018
, “
Dynamic Metamodeling for Predictive Analytics in Advanced Manufacturing
,”
Smart Sustainable Manuf. Syst.
,
2
(
1
), pp.
18
39
.10.1520/SSMS20170013
22.
Tapia
,
G.
,
Khairallah
,
S.
,
Matthews
,
M.
,
King
,
W. E.
, and
Elwany
,
A.
,
2018
, “
Gaussian Process-Based Surrogate Modeling Framework for Process Planning in Laser Powder-Bed Fusion Additive Manufacturing of 316l Stainless Steel
,”
Int. J. Adv. Manuf. Technol.
,
94
(
9–12
), pp.
3591
3603
.10.1007/s00170-017-1045-z
23.
Gaikwad
,
A.
,
Giera
,
B.
,
Guss
,
G. M.
,
Forien
,
J.-B.
,
Matthews
,
M. J.
, and
Rao
,
P.
,
2020
, “
Heterogeneous Sensing and Scientific Machine Learning for Quality Assurance in Laser Powder Bed Fusion—A Single-Track Study
,”
Addit. Manuf.
,
36
, p.
101659
.10.1016/j.addma.2020.101659
24.
Meng
,
L.
, and
Zhang
,
J.
,
2020
, “
Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model
,”
JOM
,
72
(
1
), pp.
420
428
.10.1007/s11837-019-03792-2
25.
Khairallah
,
S. A.
,
Anderson
,
A. T.
,
Rubenchik
,
A.
, and
King
,
W. E.
,
2016
, “
Laser Powder-Bed Fusion Additive Manufacturing: Physics of Complex Melt Flow and Formation Mechanisms of Pores, Spatter, and Denudation Zones
,”
Acta Mater.
,
108
, pp.
36
45
.10.1016/j.actamat.2016.02.014
26.
Wang
,
Q.
,
Michaleris
,
P. P.
,
Nassar
,
A. R.
,
Irwin
,
J. E.
,
Ren
,
Y.
, and
Stutzman
,
C. B.
,
2020
, “
Model-Based Feedforward Control of Laser Powder Bed Fusion Additive Manufacturing
,”
Addit. Manuf.
,
31
, p.
100985
.10.1016/j.addma.2019.100985
27.
Baturynska
,
I.
,
Semeniuta
,
O.
, and
Martinsen
,
K.
,
2018
, “
Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework
,”
Procedia CIRP
,
67
, pp.
227
232
.10.1016/j.procir.2017.12.204
28.
Ren
,
Y.
,
Wang
,
Q.
, and
Michaleris
,
P.
,
2019
, “
Machine-Learning Based Thermal-Geometric Predictive Modeling of Laser Powder Bed Fusion Additive Manufacturing
,”
ASME
Paper No. DSCC2019-8995
. 10.1115/DSCC2019-8995
29.
Wang
,
Q.
,
Li
,
J.
,
Gouge
,
M.
,
Nassar
,
A. R.
,
Michaleris
,
P.
, and
Reutzel
,
E. W.
,
2017
, “
Physics-Based Multivariable Modeling and Feedback Linearization Control of Melt-Pool Geometry and Temperature in Directed Energy Deposition
,”
ASME J. Manuf. Sci. Eng.
,
139
(
2
), p.
021013
.10.1115/1.4034304
30.
Li
,
J.
,
Wang
,
Q.
,
Michaleris
,
P.
,
Reutzel
,
E. W.
, and
Nassar
,
A. R.
,
2017
, “
An Extended Lumped-Parameter Model of Melt-Pool Geometry to Predict Part Height for Directed Energy Deposition
,”
ASME J. Manuf. Sci. Eng.
,
139
(
9
), p.
091016
.10.1115/1.4037235
31.
Li
,
J.
,
Wang
,
Q.
, and
Michaleris
,
P.
,
2018
, “
An Analytical Computation of Temperature Field Evolved in Directed Energy Deposition
,”
ASME J. Manuf. Sci. Eng.
,
140
(
10
), p.
101004
.10.1115/1.4040621
32.
Williams
,
C. K.
, and
Rasmussen
,
C. E.
,
2006
,
Gaussian Processes for Machine Learning
, Vol.
2
,
MIT Press
,
Cambridge, MA
.
33.
Eagar
,
T. W.
, and
Tsai
,
N.-S.
,
1983
, “
TemperatureFields Produced by Traveling Distributed Heat Sources
,”
Weld. J.
,
62
(
12
), pp.
346
355
.http://files.aws.org/wj/supplement/WJ_1983_12_s346.pdf
34.
Wang
,
Q.
,
2019
, “
A Control-Oriented Model for Melt-Pool Volume in Laser Powder Bed Fusion Additive Manufacturing
,”
ASME
Paper No. DSCC2019-9111
. 10.1115/DSCC2019-9111
35.
Gong
,
H.
,
Rafi
,
K.
,
Gu
,
H.
,
Starr
,
T.
, and
Stucker
,
B.
,
2014
, “
Analysis of Defect Generation in Ti–6Al–4V Parts Made Using Powder Bed Fusion Additive Manufacturing Processes
,”
Addit. Manuf.
,
1–4
, pp.
87
98
.10.1016/j.addma.2014.08.002
36.
Irwin
,
J. E.
,
Wang
,
Q.
,
Michaleris
,
P. P.
,
Nassar
,
A. R.
,
Ren
,
Y.
, and
Stutzman
,
C. B.
,
2021
, “
Iterative Simulation-Based Techniques for Control of Laser Powder Bed Fusion Additive Manufacturing
,”
Addit. Manuf.
,
46
, p.
102078
.10.1016/j.addma.2021.102078
37.
Heigel
,
J. C.
, and
Lane
,
B. M.
,
2018
, “
Measurement of the Melt Pool Length During Single Scan Tracks in a Commercial Laser Powder Bed Fusion Process
,”
ASME J. Manuf. Sci. Eng.
,
140
(
5
), p.
051012
.10.1115/1.4037571
38.
Rosenthal
,
D.
,
1946
, “
The Theory of Moving Sources of Heat and Its Application to Metal Treatments
,”
Trans. ASME
,
68
(
8
), pp.
849
866
.https://books.google.com/books/about/The_Theory_of_Moving_Sources_of_Heat_and.html?id=Xy8fcgAACAAJ
You do not currently have access to this content.