Yield strength prediction of high-entropy alloys using machine learning

被引：89
|
作者：
Bhandari, Uttam [1 ]
Rafi, Md Rumman [1 ]
Zhang, Congyan [1 ]
Yang, Shizhong [1 ]
机构：
[1] Southern Univ & A&M Coll, Dept Comp Sci, Baton Rouge, LA 70813 USA
来源：
MATERIALS TODAY COMMUNICATIONS | 2021年 / 26卷
关键词：
High entropy alloys; Random forest model; Yield strength prediction; MoNbTaTiW; HfMoNbTaTiZr; MECHANICAL-PROPERTIES; PHASE PREDICTION; MICROSTRUCTURE; DESIGN; SELECTION; ALUMINUM;
D O I：
10.1016/j.mtcomm.2020.101871
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Yield strength at high temperature is an important parameter in the design and application of high entropy alloys (HEAs). However, the experimental measurement of yield strength at high temperature is quite costly, complicated, and time-consuming. Therefore, it is essential to identify and apply a robust method for the accurate prediction of yield strength at high temperature from the available experimental and simulation data. In this study, for the first time, a machine learning (ML) method based on the regression technique of random forest (RF) regressor is used to predict the yield strength of HEAs at the desired temperature. The yield strengths of MoNbTaTiW and HfMoNbTaTiZr at 800 degrees C and 1200 degrees C, are predicted using the RF regressor model. We find that the results are consistent with the experimental reports, showing that the RF regressor model predicts the yield strength of HEAs at the desired temperatures with high accuracy.
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页数：5
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