Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning
点击次数：
影响因子：
8.3
DOI码：
10.1080/21663831.2020.1815093
所属单位：
中南大学粉末冶金研究院
发表刊物：
Materials Research Letters
项目来源：
中国国家重点研究与发展计划（2016YFB0701404），中国国家自然科学基金（NSFC）（91860105），中国博士后科学基金会（2019M662799）
关键字：
Superalloy high-throughput experiments, machine learning, diffusion multiple, phase selection
摘要：
Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys. IMPACT STATEMENT By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys.
合写作者：
Zi Wang, Yun-qiang Wang, Lina Zhang, Weifu Li, Jin Liu, Zexin Wang, Zihang Li, 潘军, Lei Zhao, 谭黎明, Jianxin Wang, Hua Han, Liang Jiang, Yong Liu
第一作者：
Qin, Zijun
论文类型：
Article
通讯作者：
Feng Liu
学科门类：
工学
一级学科：
冶金
卷号：
9
期号：
1
页面范围：
32-40
ISSN号：
2166-3831
是否译文：
否
学位：博士学位
职称：副研究员
学科：材料科学与工程
所在单位：粉末冶金研究院