| High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning | |
Qin, Zijun1; Li, Weifu2; Wang, Zi1; Pan, Junlong2; Wang, Zexin1; Li, Zihang1; Wang, Guowei1; Pan, Jun1; Liu, Feng1 ; Huang, Lan1; Tan, Liming3; Zhang, Lina4; Han, Hua4; Chen, Hong2; Jiang, Liang5
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| Source Publication | JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
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| ISSN | 2238-7854 |
| 2022-11-01 | |
| Volume | 21Pages:1984-1997 |
| Corresponding Author | Li, Weifu(liweifu@mail.hzau.edu.cn) ; Liu, Feng(liufeng@csu.edu.cn) |
| Abstract | 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. |
| Keyword | Superalloy Diffusion-multiple Deep learning High-throughput Powder metallurgy |
| DOI | 10.1016/j.jmrt.2022.10.032 |
| WOS Keyword | MICROSTRUCTURE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Materials Science ; Metallurgy & Metallurgical Engineering |
| WOS Subject | Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering |
| WOS ID | WOS:000878735400008 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50709 |
| Collection | 类脑智能研究中心_微观重建与智能分析 |
| Corresponding Author | Li, Weifu; Liu, Feng |
| Affiliation | 1.Cent South Univ, State Key Lab Powder Met, Changsha 410083, Peoples R China 2.Huazhong Agr Univ, Coll Sci, Wuhan 430070, Peoples R China 3.Cent South Univ, Sch Met & Environm, Changsha 410083, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Yantai Univ, Inst Adv Studies Precis Mat, Yantai 264005, Peoples R China |
| Recommended Citation 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. |
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