Selective laser melting (SLM), an emerging technology, constructs components through layer-by-layer material deposition and has gained popularity in the industry due to its advantages such as shorter lead time, higher flexibility, lower material wastage, and the capability to fabricate complex geometries. However, the development of process databases for new materials is often time-consuming and laborious because SLM involves multiple physical fields and multiple process steps with numerous process parameters. Recently, machine learning is renowned for its excellent capabilities in tasks such as classification, regression, and clustering. In this study, hybrid Gaussian boosted regression that combines Gaussian process regression with gradient boosting machine was used to obtain a process database for CuCrZr alloy, optimizing for density with laser power and scanning speed as characteristic parameters, under limited samples. A machine learning model was developed using fivefold cross-training on 36 datasets. With a determination coefficient (R2) of 0.96587, the model demonstrated a high level of fit. Next, by extending the prediction range, we achieved process parameters for the highest five densities of samples. Finally, the model’s precision was confirmed with experiments on the five predicted maximum densities, with all predictions falling within a ±0.09% error margin from the experimental values. This research precisely predicted the densities of SLM-formed CuCrZr parts, created a comprehensive process parameter database, and substantiated both theoretical and practical backing for the 3D printing of CuCrZr parts.

1.
N.
Guo
and
M. C.
Leu
, “
Additive manufacturing: Technology, applications and research needs
,”
Front. Mech. Eng.
8
,
215
243
(
2013
).
2.
J. C.
Najmon
,
S.
Raeisi
, and
A.
Tovar
, “
Review of additive manufacturing technologies and applications in the aerospace industry
,”
Addit. Manuf. Aerosp. Ind.
2019
,
7
31
.
3.
S.
Singh
,
G.
Singh
,
C.
Prakash
, and
S.
Ramakrishna
, “
Current status and future directions of fused filament fabrication
,”
J. Manuf. Process.
55
,
288
306
(
2020
).
4.
B.
Wu
,
Z.
Pan
,
D.
Ding
,
D.
Cuiuri
,
H.
Li
,
J.
Xu
, and
J.
Norrish
, “
A review of the wire arc additive manufacturing of metals: Properties, defects and quality improvement
,”
J. Manuf. Process.
35
,
127
139
(
2018
).
5.
E.
Liverani
,
A.
Fortunato
,
A.
Leardini
,
C.
Belvedere
,
S.
Siegler
,
L.
Ceschini
, and
A.
Ascari
, “
Fabrication of Co–Cr–Mo endoprosthetic ankle devices by means of selective laser melting (SLM)
,”
Mater. Des.
106
,
60
68
(
2016
).
6.
A.
Shang
,
B.
Stegman
,
D.
Sinclair
, X. Sheng, L. Hoppenrath, C. Shen, K. Xu, E. Flores, H. Wang, N. Chawla, and X. Zhang, “
Crack mitigation strategies for a high-strength Al alloy Al92Ti2Fe2Co2Ni2 fabricated by additive manufacturing
,”
J. Mater. Res. Technol.
30
,
5497
5511
(
2024
).
7.
V.
Maitra
,
J.
Shi
, and
C.
Lu
, “
Robust prediction and validation of as-built density of Ti-6Al-4V parts manufactured via selective laser melting using a machine learning approach
,”
J. Manuf. Process.
78
,
183
201
(
2022
).
8.
V.
Maitra
and
J.
Shi
, “
Surface roughness prediction for additively manufactured Ti-6Al-4V components based on supervised learning models
,” in
Proceedings of the International Manufacturing Science and Engineering Conference
, West Lafayette, IN, June 27–July 1, 2022 (
American Society of Mechanical Engineers
, New York,
2022
).
9.
S.
Jin
,
A.
Iquebal
,
S.
Bukkapatnam
,
A.
Gaynor
, and
Y.
Ding
, “
A Gaussian process model-guided surface polishing process in additive manufacturing
,”
J. Manuf. Sci. Eng.
142
,
011003
(
2020
).
10.
C.
Galy
,
E.
Le Guen
,
E.
Lacoste
, and
C.
Arvieu
, “
Main defects observed in aluminum alloy parts produced by SLM: From causes to consequences
,”
Addit. Manuf.
22
,
165
175
(
2018
).
11.
J.
Bi
,
L.
Wu
,
Z.
Liu
, H. Wang, X. Jia, X. Chen, M. D. Starostenkov, and G. Dong, “
Formability, surface quality and compressive fracture behavior of AlMgScZr alloy lattice structure fabricated by selective laser melting
,”
J. Mater. Res. Technol.
19
,
391
403
(
2022
).
12.
V. P.
Sabelkin
,
G. R.
Cobb
,
T. E.
Shelton
,
M. N.
Hartsfield
,
D. J.
Newell
,
R. P.
O’Hara
, and
R.
Kemnitz
, “
Mitigation of anisotropic fatigue in nickel alloy 718 manufactured via selective laser melting
,”
Mater. Des.
182
,
108095
(
2019
).
13.
D.
Ren
,
H.
Zhang
,
Y.
Liu
,
S. J.
Li
,
W.
Jin
,
R.
Yang
, and
L. C.
Zhang
, “
Microstructure and properties of equiatomic Ti–Ni alloy fabricated by selective laser melting
,”
Mater. Sci. Eng. A
771
,
138586
(
2020
).
14.
C.
Wallis
and
B.
Buchmayr
, “
Effect of heat treatments on microstructure and properties of CuCrZr produced by laser-powder bed fusion
,”
Mater. Sci. Eng. A
744
,
215
223
(
2019
).
15.
Y.
Bai
,
C.
Zhao
,
Y.
Zhang
,
J.
Chen
, and
H.
Wang
, “
Additively manufactured CuCrZr alloy: Microstructure, mechanical properties and machinability
,”
Mater. Sci. Eng. A
819
,
141528
(
2021
).
16.
Z.
Hu
,
Z.
Du
,
Z.
Yang
,
L.
Yu
, and
Z.
Ma
, “
Preparation of Cu–Cr–Zr alloy by selective laser melting: Role of scanning parameters on densification, microstructure and mechanical properties
,”
Mater. Sci. Eng. A
836
,
142740
(
2022
).
17.
Z.
Kuai
,
Z.
Li
,
B.
Liu
,
Y.
Chen
,
S.
Lu
,
P.
Bai
, “
Effect of heat treatment on CuCrZr alloy fabricated by selective laser melting: Microstructure evolution, mechanical properties and fracture mechanism
,”
J. Mater. Res. Technol.
23
,
2658
2671
(
2023
).
18.
B.
Zhang
,
L.
Dembinski
, and
C.
Coddet
, “
The study of the laser parameters and environment variables effect on mechanical properties of high compact parts elaborated by selective laser melting 316L powder
,”
Mater. Sci. Eng. A
584
,
21
31
(
2013
).
19.
M.
Zavala-Arredondo
,
T.
London
,
M.
Allen
, T. Maccio, S. Ward, D. Griffiths, A. Allison, P. Goodwin, and C. Hauser, “
Use of power factor and specific point energy as design parameters in laser powder-bed-fusion (L-PBF) of AlSi10Mg alloy
,”
Mater. Des.
182
,
108018
(
2019
).
20.
C.
Wang
,
X.
Tan
,
S. B.
Tor
, and
C. S.
Lim
, “
Machine learning in additive manufacturing state-of-the-art and perspectives
,”
Addit. Manuf.
36
,
101538
(
2020
).
21.
N.
Johnson
,
P.
Vulimiri
,
A.
To
,
X.
Zhang
,
C. A.
Brice
,
B. B.
Kappes
, and
A. P.
Stebner
, “
Invited review: Machine learning for materials developments in metals additive manufacturing
,”
Addit. Manuf.
36
,
101641
(
2020
).
22.
Y.
Chen
,
H.
Wang
,
Y.
Wu
, and H. Wang, “
Predicting the printability in selective laser melting with a supervised machine learning method
,”
Materials
13
,
5063
(
2020
).
23.
G. O.
Barrionuevo
,
J. A.
Ramos-Grez
,
M.
Walczak
, and
C. A.
Betancourt
, “
Comparative evaluation of supervised machine learning algorithms in the prediction of the relative density of 316L stainless steel fabricated by selective laser melting
,”
Int. J. Adv. Manuf. Technol.
113
,
419
433
(
2021
).
24.
S.
Chaudhry
and
A.
Soulaïmani
, “
A comparative study of machine learning methods for computational modeling of the selective laser melting additive manufacturing process
,”
Appl. Sci.
12
,
2324
(
2022
).
25.
N. S.
Ross
,
P. M.
Mashinini
,
P.
Mishra
, M. B. J. Ananth, S. M. Mustafa, M. K. Gupta, M. E. Korkmaz, and A. Nag, “
Enhancing surface quality and tool life in SLM-machined components with dual-MQL approach
,”
J. Mater. Res. Technol.
31
,
1837
1852
(
2024
).
26.
Z.
Hu
and
S.
Mahadevan
, “
Uncertainty quantification and management in additive manufacturing: Current status, needs, and opportunities
,”
Int. J. Adv. Manuf. Technol.
93
,
2855
2874
(
2017
).
27.
J. A.
Lee
,
M. J.
Sagong
,
J.
Jung
,
E. S.
Kim
, and
H. S.
Kim
, “
Explainable machine learning for understanding and predicting geometry and defect types in Fe–Ni alloys fabricated by laser metal deposition additive manufacturing
,”
J. Mater. Res. Technol.
22
,
413
423
(
2023
).
28.
G.
Tapia
,
S.
Khairallah
,
M.
Matthews
,
W. E.
King
, and
A.
Elwany
, “
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
,
3591
3603
(
2018
).
29.
G.
Tapia
,
A. H.
Elwany
, and
H.
Sang
, “
Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models
,”
Addit. Manuf.
12
,
282
290
(
2016
).
30.
S.
Jabarzadeh
,
A.
Ghasemi-Ghalebahman
, and
A.
Najibi
, “
Investigation into microstructure, mechanical properties, and compressive failure of functionally graded porous cylinders fabricated by SLM
,”
Eng. Failure Anal.
165
,
108794
(
2024
).
31.
T.
Larimian
,
M.
Kannan
,
D.
Grzesiak
,
B.
AlMangour
, and
T.
Borkar
, “
Effect of energy density and scanning strategy on densification, microstructure and mechanical properties of 316L stainless steel processed via selective laser melting
,”
Mater. Sci. Eng. A
770
,
138455
(
2020
).
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