Laser powder bed fusion process optimization of CoCrMo alloy assisted by machine-learning
Gaussian process regression (GPR) model of machine learning method was employed to identify the optimal process window for high-performance CoCrMo alloy in laser powder bed fusion (LPBF), considering density (≥99%) and surface roughness (≤7 μm) as key parameters. Additionally, the study examined the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-11-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785424023457 |
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| Summary: | Gaussian process regression (GPR) model of machine learning method was employed to identify the optimal process window for high-performance CoCrMo alloy in laser powder bed fusion (LPBF), considering density (≥99%) and surface roughness (≤7 μm) as key parameters. Additionally, the study examined the impact of LPBF parameters on morphology and distribution of defect and surface roughness. Results revealed a tongue-shaped optimal process window, with scanning speed having a greater influence on density than laser power. High laser power reduced surface roughness, and a combination of medium-to-high laser power (160–340 W) and moderate scanning speed (600–1500 mm/s) achieved low surface roughness (Ra ≤ 7 μm). The mean absolute error confirmed the reliability of the optimized process window predicted by GPR. |
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| ISSN: | 2238-7854 |