Machine learning enabled processing map generation for high-entropy alloy

被引：21

作者：

Kumar, Saphal ^{[1
]}

Pradhan, Hrutidipan ^{[1
]}

Shah, Naishalkumar ^{[2
]}

Rahul, M. R. ^{[1
]}

Phanikumar, Gandham ^{[2
]}

机构：

[1] Indian Inst Technol ISM Dhanbad, Dept Fuel Minerals & Met Engn, Dhanbad 826004, Jharkhand, India

[2] Indian Inst Technol Madras, Dept Met & Mat Engn, Chennai 600036, Tamil Nadu, India

来源：

SCRIPTA MATERIALIA | 2023年 / 234卷

关键词：

Processing maps; Eutectic high entropy alloys; Machine learning; Hot deformation; HOT DEFORMATION; PHASE;

D O I：

10.1016/j.scriptamat.2023.115543

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Identifying optimum processing conditions is necessary for new material development. The flow curves can be used to develop the processing map for an alloy. The current study trained multiple machine learning models such as Random Forest Regressor (RFR), K Nearest Neighbors (KNN), Extra Tree Regressor (ETR) and Artiflcial Neural Network (ANN) to predict the flow behaviour of the material. The testing R2 flt score of more than 0.99 was obtained for all four algorithms, and trained models were used to generate the flow curves at various temperature strain rate combinations for CoCrFeNiTa0.395 eutectic high entropy alloy. A processing map was developed using the results from ANN and validated with the experimental microstructure observations.

引用

页数：6

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