Machine learning accelerated design of a family of AlxCrFeNi medium entropy alloys with superior high temperature mechanical and oxidation properties

Machine learning accelerated design of a family of AlxCrFeNi medium entropy alloys with superior high temperature mechanical and oxidation properties

Corrosion Science

10.1016/j.corsci.2022.110805

http://dx.doi.org/10.1016/j.corsci.2022.110805

Ling Qiao, R.V. Ramanujan, Jingchuan Zhu

21 November 2022

Qiao, L., Ramanujan, R. V., Zhu, J. (2023). Machine learning accelerated design of a family of AlxCrFeNi medium entropy alloys with superior high temperature mechanical and oxidation properties. Corrosion Science, 211, 110805. https://doi.org/10.1016/j.corsci.2022.110805

This work implemented machine learning (ML) approach to map the relationship between temperature, alloying elements and yield strength in multi-component alloys. Then AlxCrFeNi medium-entropy alloys (MEAs) were developed and a two-phase structure, formed by the spinodal decomposition mechanism, was observed. With increasing Al content, the high temperature mechanical properties dramatically improved. Our developed AlxCrFeNi MEAs (x>0.8) oer low density and excellent mechanical properties, superior to conventional alloys. The oxidation behavior of AlxCrFeNi MEAs (x>0.8) at 1000C was explored and the oxidation mechanism was identified. This work has identified a promising family of MEAs for high temperature structural applications.

Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

This research / project is supported by the Agency for Science, Technology and Research - Structural Metal Alloys Programme
Grant Reference no. : A18B1b0061

This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Fund
Grant Reference no. : A1898b0043

https://oar.a-star.edu.sg/communities-collections/articles/20910

0010-938X

Institute of Materials Research & Engineering