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The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization

The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization

Authors :
Pei Liu
Haiyou Huang
Cheng Wen
Turab Lookman
Yanjing Su
Source :
npj Computational Materials, Vol 9, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size, and morphology in CoNiAlCr-based superalloys. The effectiveness of the algorithm is demonstrated by synthesizing alloys with desired γ/γ′ microstructure and optimizing γ′ microstructural parameters. In addition, the method leads to incorporating refractory elements to improve γ/γ′ microstructure in superalloys. After four iterations of experiments guided by the algorithm, we synthesize sixteen alloys of relatively high creep strength from ~120,000 candidates of which three possess high γ′ volume fraction (>54%), small γ′ size (77%).

Details

Language :
English
ISSN :
20573960
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
npj Computational Materials
Publication Type :
Academic Journal
Accession number :
edsdoj.30cb8eaf152c44ca9cd6af08be17e885
Document Type :
article
Full Text :
https://doi.org/10.1038/s41524-023-01090-9