收稿日期: 2022-02-25
网络出版日期: 2022-05-27
基金资助
国家重点研发计划资助项目(2018YFB0704400);云南省重大科技专项资助项目(202002AB080001-2);之江实验室科研攻关资助项目(2021PE0AC02)
Phase stability prediction of hign entropy alloys in aluminum matrix composites based on feature engneering and machine learning
Received date: 2022-02-25
Online published: 2022-05-27
胡瑞, 刘庆, 张光捷, 李俊杰, 陈晓玉, 魏晓, 戴东波 . 基于特征工程和机器学习的铝基高熵合金稳定性预测[J]. 上海大学学报(自然科学版), 2022 , 28(3) : 476 -484 . DOI: 10.12066/j.issn.1007-2861.2381
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.
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