Composition Design of High Strength ODS Alloy Based on Machine Learning

被引：0
作者：
Bai B. [1 ]
Zheng Q. [1 ]
Ren S. [2 ]
Zhang C. [1 ]
Yang W. [1 ]
Hu C. [2 ]
机构：
[1] Division of Reactor Engineering Technology Research, China Institute of Atomic Energy, Beijing
[2] University of Science and Technology Beijing, Beijing
来源：
Yang, Wen (ywhyangwen@163.com) | 1600年 / Atomic Energy Press卷 / 54期
关键词：
Machine learning; Material optimization; ODS alloy; Tensile property;
D O I：
10.7538/yzk.2019.youxian.0552
中图分类号：
学科分类号：
摘要：
Based on 200-300 groups data of compositions, processes and mechanical properties, the relationship between the key parameters and tensile property of oxide dispersion strengthened (ODS) alloy was established by machine learning. The results show that the optimum value corresponding to the maximum strength exists in the relationship between the content of Cr, Y2O3, W and Ti and the tensile strength of ODS alloy. The addition of Al has no obvious effect on the increase of tensile strength. Therefore, several optimum compositions of ODS alloy were obtained, and the predicted tensile strength at room temperature is above 1 400 MPa. It can help to promote the optimization of ODS alloy as cladding material for fast reactor application. © 2020, Editorial Board of Atomic Energy Science and Technology. All right reserved.
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页码：678 / 682
页数：4
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