1. Machine learning-assisted composition design of W-free Co-based superalloys with high γ′-solvus temperature and low density
- Author
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Linlin Sun, Bin Cao, Qingshuang Ma, Qiuzhi Gao, Jiahao Luo, Minglong Gong, Jing Bai, and Huijun Li
- Subjects
W-free Co-based superalloys ,Machine learning ,Dual-target optimization ,γ′-solvus temperature ,Alloy density ,Mining engineering. Metallurgy ,TN1-997 - Abstract
Developing materials with multiple desired characteristics is a tremendous challenge, particularly in an elaborate material system. Herein, a machine learning assisted material design strategy was applied to simultaneously optimize dual target attributes by considering γ′ solvus temperature and alloy density of multi-component Co-based superalloys. To verify the soundness of our strategy, four alloys were selected and experimentally synthesized from >510,000 candidates, each of them possessing γ′ solvus temperature exceeding 1200 °C and alloy density below 8.3 g/cm3. Of those, Co-35Ni-12Al-5Ti-3V-3Cr-2Ta-2Mo (at.%) possesses the highest γ′ solvus temperature of 1250 °C and lower density of 8.2 g/cm3. This article validates a straightforward strategy to guide rapid discovery and fabrication of multi-component materials with desired dual-performance characteristics.
- Published
- 2024
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