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

doi:10.1007/s11771-022-5158-7

IMR OpenIR

	Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network
	Wu Jia-jun 1,2; Huang Zheng 1,2; Qiao Hong-chao 1,2; Wei Bo-xin 3,4; Zhao Yong-jie 5; Li Jing-feng 6; Zhao Ji-bin 1,2
通讯作者	Huang Zheng(huangzheng@ustc.edu.cn) ; Zhao Ji-bin(jbzhao@sia.cn)
	2022-11-02
发表期刊	JOURNAL OF CENTRAL SOUTH UNIVERSITY
ISSN	2095-2899
页码	15
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.
关键词	laser shock processing residual stress microhardness artificial neural network
资助者	National Natural Science Foundation of China ; National Key R&D Program of China
DOI	10.1007/s11771-022-5158-7
收录类别	SCI
语种	英语
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
WOS类目	Metallurgy & Metallurgical Engineering
WOS记录号	WOS:000878011200002
出版者	JOURNAL OF CENTRAL SOUTH UNIV
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/176489
专题	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.