High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning | |
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 |
2022-11-01 | |
卷号 | 21页码:1984-1997 |
关键词 | Superalloy Diffusion-multiple Deep learning High-throughput Powder metallurgy |
ISSN号 | 2238-7854 |
DOI | 10.1016/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. |
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关键词 | MICROSTRUCTURE |
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 |
内容类型 | 期刊论文 |
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. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论