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

doi:10.1016/j.jmrt.2022.10.032

	High-throughput characterization methods for Ni-based superalloys and phase prediction via deep learning
	Qin, Zijun 1; Li, Weifu 2; Wang, Zi 1; Pan, Junlong 2; Wang, Zexin 1; Li, Zihang 1; Wang, Guowei 1; Pan, Jun 1; Liu, Feng1 ; Huang, Lan 1; Tan, Liming 3; Zhang, Lina 4; Han, Hua4 ; Chen, Hong 2; Jiang, Liang 5
发表期刊	JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
ISSN	2238-7854
	2022-11-01
卷号	21 页码:1984-1997
通讯作者	Li, Weifu(liweifu@mail.hzau.edu.cn) ; Liu, Feng(liufeng@csu.edu.cn)
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.
关键词	Superalloy Diffusion-multiple Deep learning High-throughput Powder metallurgy
DOI	10.1016/j.jmrt.2022.10.032
关键词[WOS]	MICROSTRUCTURE
收录类别	SCI
语种	英语
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]
项目资助者	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研究方向	Materials Science ; Metallurgy & Metallurgical Engineering
WOS类目	Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
WOS记录号	WOS:000878735400008
出版者	ELSEVIER
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50709
专题	脑图谱与类脑智能实验室_微观重建与智能分析
通讯作者	Li, Weifu; Liu, Feng
作者单位	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
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.