中文

Journal of Shanghai University >

2022 , Vol. 28 >Issue 3: 476 - 484

DOI: https://doi.org/10.12066/j.issn.1007-2861.2381

Machine Learning

Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning

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  • 1. School Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2. Centerof Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China
    3. Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China

Received date: 2022-02-25

  Online published: 2022-05-27

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Abstract

Aluminum matrix composites offer many excellent properties and wide application prospects. High entropy alloys with a simple and stable phase can be used as reinforcement to prepare aluminum matrix composites with significantly improved performance in all aspects. Herein, a new method based on feature engineering and machine learning is proposed to investigate the phase stability of high entropy alloys. This method uses feature engineering to determine the important factors affecting the target attributes, and then selects the corresponding regression method to predict the phase stability. A model on 50% of the datasets is trained and then the model is verified on other datasets. The results show that this method is highly accurate in predicting the phase stability of high entropy alloys ($R^2=0.994$). In addition, this method can be used to identify key factors affecting phase stability.

Key words： aluminum matrix composite; high entropy alloy; feature engineering; machine learning; phase stability prediction

Cite this article

HU Rui, LIU Qing, ZHANG Guangjie, LI Junjie, CHEN Xiaoyu, WEI Xiao, DAI Dongbo . Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning[J]. Journal of Shanghai University, 2022 , 28(3) : 476 -484 . DOI: 10.12066/j.issn.1007-2861.2381

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