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Design of comprehensive mechanical properties by machine learning and high-throughput optimization algorithm in RAFM steels

  • Wang, Chenchong (State Key Laboratory of Rolling and Automation, School of Materials Science and Engineering, Northeastern University) ;
  • Shen, Chunguang (State Key Laboratory of Rolling and Automation, School of Materials Science and Engineering, Northeastern University) ;
  • Huo, Xiaojie (Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University) ;
  • Zhang, Chi (Key Laboratory of Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University) ;
  • Xu, Wei (State Key Laboratory of Rolling and Automation, School of Materials Science and Engineering, Northeastern University)
  • Received : 2019.08.11
  • Accepted : 2019.10.21
  • Published : 2020.05.25

Abstract

In order to make reasonable design for the improvement of comprehensive mechanical properties of RAFM steels, the design system with both machine learning and high-throughput optimization algorithm was established. As the basis of the design system, a dataset of RAFM steels was compiled from previous literatures. Then, feature engineering guided random forests regressors were trained by the dataset and NSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution set with nine composition features and two treatment processing features. The selected optimal solutions by this design system showed prospective mechanical properties, which was also consistent with the physical metallurgy theory. This efficiency design mode could give the enlightenment for the design of other metal structural materials with the requirement of multi-properties.