Machine learning-assisted composition design of W-free Co-based superalloys with high γ′-solvus temperature and low density
Publication Name
Journal of Materials Research and Technology
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.
Open Access Status
This publication may be available as open access
Volume
29
First Page
656
Last Page
667
Funding Number
CXZZBS2023164
Funding Sponsor
National Natural Science Foundation of China