Impact Factor:8.3
DOI number:10.1080/21663831.2020.1815093
Affiliation of Author(s):中南大学粉末冶金研究院
Teaching and Research Group:高温结构材料研究所
Journal:Materials Research Letters
Funded by: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）
Key Words:Superalloy high-throughput experiments, machine learning, diffusion multiple, phase selection
Abstract: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.
Co-author:Zi Wang, Yun-qiang Wang, Lina Zhang, Weifu Li, Jin Liu, Zexin Wang, Zihang Li, Jun Pan, Lei Zhao, Liming Tan, Jianxin Wang, Hua Han, Liang Jiang, Yong Liu
First Author:Qin, Zijun
Indexed by:Article
Correspondence Author:Feng Liu
Discipline:Engineering
First-Level Discipline:冶金
Volume:9
Issue:1
Page Number:32-40
ISSN No.:2166-3831
Translation or Not:no