基于特征工程和机器学习的铝基高熵合金稳定性预测

doi:10.12066/j.issn.1007-2861.2381

Abstract

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

CLC Number:

TB 331

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（Natural Science Edition）, 2022, 28(3): 476-484.

Figures/Tables 8

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References 30

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

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