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High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning

文献类型：期刊论文

作者Qin, Zijun5; Li, Weifu4; Wang, Zi5; Pan, Junlong4; Wang, Zexin5; Li, Zihang5; Wang, Guowei5; Pan, Jun5; Liu, Feng5; Huang, Lan5
刊名JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
出版日期2022-11-01
卷号21页码:1984-1997
ISSN号2238-7854
关键词Superalloy Diffusion-multiple Deep learning High-throughput Powder metallurgy
DOI10.1016/j.jmrt.2022.10.032
通讯作者Li, Weifu(liweifu@mail.hzau.edu.cn) ; Liu, Feng(liufeng@csu.edu.cn)
英文摘要The strengthening phases characteristics in the alloy determine the mechanical properties of the alloy, but it is a hard task to predict the precipitation of complex alloys. In this work, we quickly detected 33,484 groups of Ni-based superalloys composition information and microstructure image by integrating high-throughput experiment and a nested UNet 3+ architecture for image recognition, and established a database of gamma' precipitation. Based on the database, a high-confidence prediction model was established, which could accurately predict the volume fraction, average size and size distribution of gamma' prediction in different alloys. Compared with the traditional methods, the proposed approach has a remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multi-component alloys. (C) 2022 The Author(s). Published by Elsevier B.V.
WOS关键词MICROSTRUCTURE
资助项目National Science and Technology Major Project ; National Natural Sci-ence Foundation of China ; Natural Sci-ence Foundation of Hunan Province of China ; Science and Technology Innovation Program of Hunan Prov-ince ; Fundamental Research Funds for the Central Universities of China ; State Key Labo-ratory of Powder Metallurgy, Central South University, Changsha, China ; [J2019-IV-0003-0070] ; [91860105] ; [52074366] ; [2021JJ40757] ; [2021RC3131] ; [2662020LXQD002]
WOS研究方向Materials Science ; Metallurgy & Metallurgical Engineering
语种英语
出版者ELSEVIER
WOS记录号WOS:000878735400008
资助机构National Science and Technology Major Project ; National Natural Sci-ence Foundation of China ; Natural Sci-ence Foundation of Hunan Province of China ; Science and Technology Innovation Program of Hunan Prov-ince ; Fundamental Research Funds for the Central Universities of China ; State Key Labo-ratory of Powder Metallurgy, Central South University, Changsha, China
源URL[http://ir.ia.ac.cn/handle/173211/50708]  
专题类脑智能研究中心_微观重建与智能分析
通讯作者Li, Weifu; Liu, Feng
作者单位1.Yantai Univ, Inst Adv Studies Precis Mat, Yantai 264005, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Cent South Univ, Sch Met & Environm, Changsha 410083, Peoples R China
4.Huazhong Agr Univ, Coll Sci, Wuhan 430070, Peoples R China
5.Cent South Univ, State Key Lab Powder Met, Changsha 410083, Peoples R China
推荐引用方式
GB/T 7714		 Qin, Zijun,Li, Weifu,Wang, Zi,et al. High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning[J]. JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T,2022,21:1984-1997.
APA Qin, Zijun.,Li, Weifu.,Wang, Zi.,Pan, Junlong.,Wang, Zexin.,...&Jiang, Liang.(2022).High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning.JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T,21,1984-1997.
MLA Qin, Zijun,et al."High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning".JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 21(2022):1984-1997.

入库方式： OAI收割

来源：自动化研究所

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