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Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network

文献类型：期刊论文

作者Wu Jia-jun1,2; Huang Zheng1,2; Qiao Hong-chao1,2; Wei Bo-xin3,4; Zhao Yong-jie5; Li Jing-feng6; Zhao Ji-bin1,2
刊名JOURNAL OF CENTRAL SOUTH UNIVERSITY
出版日期2022-11-02
页码15
关键词laser shock processing residual stress microhardness artificial neural network
ISSN号2095-2899
DOI10.1007/s11771-022-5158-7
通讯作者Huang Zheng(huangzheng@ustc.edu.cn) ; Zhao Ji-bin(jbzhao@sia.cn)
英文摘要In this work, the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material, and the experimental parameters in multiple overlap laser shock processing (LSP) treatment were selected based on orthogonal experimental design. The experimental data of residual stress and microhardness were measured in the same depth. The residual stress and microhardness laws were investigated and analyzed. Artificial neural network (ANN) with four layers (4-N-(N-1)-2) was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap LSP. The experimental data were divided as training-testing sets in pairs. Laser energy, overlap rate, shocked times and depth were set as inputs, while residual stress and microhardness were set as outputs. The prediction performances with different network configuration of developed ANN models were compared and analyzed. The developed ANN model with network configuration of 4-7-6-2 showed the best predict performance. The predicted values showed a good agreement with the experimental values. In addition, the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were studied. It can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.
资助项目National Natural Science Foundation of China[51875558] ; National Natural Science Foundation of China[51471176] ; National Key R&D Program of China[2017YFB1302802]
WOS研究方向Metallurgy & Metallurgical Engineering
语种英语
WOS记录号WOS:000878011200002
出版者JOURNAL OF CENTRAL SOUTH UNIV
资助机构National Natural Science Foundation of China ; National Key R&D Program of China
源URL[http://ir.imr.ac.cn/handle/321006/176488]  
专题金属研究所_中国科学院金属研究所
通讯作者Huang Zheng; Zhao Ji-bin
作者单位1.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Peoples R China
2.Chinese Acad Sci, Inst Robot, Shenyang 110169, Peoples R China
3.Chinese Acad Sci, Inst Met Res, Shenyang 110016, Peoples R China
4.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China
5.Univ Hull, Fac Sci & Engn, Kingston Upon Hull HU6 7RX, N Humberside, England
6.Tsinghua Univ, Dept Chem, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Wu Jia-jun,Huang Zheng,Qiao Hong-chao,et al. Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network[J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY,2022:15.
APA Wu Jia-jun.,Huang Zheng.,Qiao Hong-chao.,Wei Bo-xin.,Zhao Yong-jie.,...&Zhao Ji-bin.(2022).Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network.JOURNAL OF CENTRAL SOUTH UNIVERSITY,15.
MLA Wu Jia-jun,et al."Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network".JOURNAL OF CENTRAL SOUTH UNIVERSITY (2022):15.

入库方式： OAI收割

来源：金属研究所

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