摘要
Lightweight refractory high-entropy alloys(LW-RHEAs)hold significant potential in the fields of aviation,aerospace,and nuclear energy due to their low density,high strength,high hardness,and corrosion resistance.However,the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance.In this paper,an machine learning(ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs.The quantitative relation of“composition-structure-property”was first established by ML modeling.Then,feature analysis reveals that Cr content greater than 12 at.
基金
The financial support from the Natural Science Foundation of Hunan Province for Distinguished Young Scholars,China[GrantNo.2021JJ10062]
the Science and Technology Program of Guangxi province,China[GrantNo.AB21220028]
the Youth Fund of the National Natural Science Foundationof China[Grant No.52401047,Grant No.52401004]
the China Postdoctoral Science Foundation,China[GrantNo.2023M741244]
the Fundamental Research Funds for the Central Universities of Central South University,China[GrantNo.2023ZZTS0711]are acknowledged
The Project supported by State Key Laboratory of Powder Metallurgy,Central South University,Changsha,China is also acknowledged.
