Machine learning-assisted discovery of Cr, Al-containing high-entropy alloys for high oxidation resistance

被引：16

作者：

Dong, Ziqiang ^{[1
]}

Sun, Ankang ^{[1
]}

Yang, Shuang ^{[1
]}

Yu, Xiaodong ^{[1
]}

Yuan, Hao ^{[1
]}

Wang, Zihan ^{[1
]}

Deng, Luchen ^{[1
]}

Song, Jinxia ^{[2
]}

Wang, Dinggang ^{[2
]}

Kang, Yongwang ^{[2
]}

机构：

[1] Shanghai Univ, Mat Genome Inst, Shanghai 200444, Peoples R China

[2] Beijing Inst Aeronaut Mat, Sci & Technol Adv High Temp Struct Mat Lab, Beijing 100095, Peoples R China

来源：

CORROSION SCIENCE | 2023年 / 220卷

关键词：

Machine learning; High-entropy alloys; High-temperature oxidation; MECHANICAL-PROPERTIES; BEHAVIOR; MICROSTRUCTURE; WEAR; X=0;

D O I：

10.1016/j.corsci.2023.111222

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

A Machine Learning (ML) integrated workflow was utilized to guide the design of Cr, Al-containing five-element high-entropy alloys (HEAs) for achieving an enhanced high-temperature oxidation resistance. ML directs the design of HEAs to a chemical composition consisting of Fe, Cr, Al, Ni, and Cu for enhanced oxidation resistance. The oxidation behavior of AlxCrCuFeNi (x = 0, 0.25, 0.5, 1) HEAs at 1100 degrees C in air was systematically inves-tigated and the oxidation mechanism was elucidated. The experimental validation agrees well with the ML prediction, demonstrating that ML could be used as a powerful tool for designing alloys with optimized oxidation resistance.

引用

页数：16

共 59 条

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