Prediction and design of high hardness high entropy alloy through machine learning

[Display omitted] •Particle swarm algorithm is used to inversely optimize the alloy composition according to target performance.•An optimized feature set including 3 features is obtained by GA, interpretable ML and solid-solution strengthening.•A general design framework is summarized for prediction...

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Vydané v:Materials & design Ročník 235; s. 112454
Hlavní autori: Ren, Wei, Zhang, Yi-Fan, Wang, Wei-Li, Ding, Shu-Jian, Li, Nan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2023
Elsevier
Predmet:
High-entropy alloy
Intelligent optimization algorithm
Interpretable machine learning
Machine learning
Solid solution strengthening
Interpretable machine learning
Solid solution strengthening
Intelligent optimization algorithm
High-entropy alloy
Machine learning
ISSN:0264-1275, 1873-4197
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Shrnutí:[Display omitted] •Particle swarm algorithm is used to inversely optimize the alloy composition according to target performance.•An optimized feature set including 3 features is obtained by GA, interpretable ML and solid-solution strengthening.•A general design framework is summarized for prediction and composition optimization of various HEA performances. Two data-driven machine learning (ML) models were proposed for the hardness prediction of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs, respectively. The hardness prediction model combined interpretable ML methods with solid solution strengthening theory, and the R2 and RMSE values of 0.9716 and 39.2525 were respectively achieved under the leave-one-out validation method. The optimization model adopted an intelligent optimization algorithm to design the optimized elemental molar ratios of high hardness HEAs and was experimentally verified. A general design framework was summarized for prediction and composition optimization of various HEA performances.
ISSN:0264-1275
1873-4197
DOI:10.1016/j.matdes.2023.112454