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.
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February 2025
Research Article|
January 13 2025
Density prediction for selective laser melting fabricated of CuCrZr alloy using hybrid Gaussian boosted regression
Guangzhao Yang
;
Guangzhao Yang
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
School of Mechanical and Automation Engineering, Wuyi University
, Jiangmen 529020, China
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Mingxuan Cao
;
Mingxuan Cao
a)
(Conceptualization, Funding acquisition, Writing – review & editing)
1
School of Mechanical and Automation Engineering, Wuyi University
, Jiangmen 529020, China
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Yixun Cai;
Yixun Cai
(Data curation)
1
School of Mechanical and Automation Engineering, Wuyi University
, Jiangmen 529020, China
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Baojian Yang;
Baojian Yang
b)
(Formal analysis)
1
School of Mechanical and Automation Engineering, Wuyi University
, Jiangmen 529020, China
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Honghai Gan;
Honghai Gan
(Data curation, Supervision)
1
School of Mechanical and Automation Engineering, Wuyi University
, Jiangmen 529020, China
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Bin Fu;
Bin Fu
(Investigation, Project administration)
2
School of Innovation and Entrepreneurship, Wuyi University
, Jiangmen 529020, China
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Liang Li;
Liang Li
(Data curation)
2
School of Innovation and Entrepreneurship, Wuyi University
, Jiangmen 529020, China
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Ying Wang;
Ying Wang
(Conceptualization)
3
Optton Co., Ltd.
, Shanghai 2000000, China
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Matthew M. F. Yuen
Matthew M. F. Yuen
(Methodology, Supervision)
4
Department of Mechanical Engineering, Hong Kong University of Science and Technology
, Hong Kong 999077, China
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a)
Electronic mail: [email protected]
b)
Electronic mail: [email protected]
J. Laser Appl. 37, 012023 (2025)
Article history
Received:
April 29 2024
Accepted:
December 21 2024
Citation
Guangzhao Yang, Mingxuan Cao, Yixun Cai, Baojian Yang, Honghai Gan, Bin Fu, Liang Li, Ying Wang, Matthew M. F. Yuen; Density prediction for selective laser melting fabricated of CuCrZr alloy using hybrid Gaussian boosted regression. J. Laser Appl. 1 February 2025; 37 (1): 012023. https://doi.org/10.2351/7.0001414
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