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

作     者：WU Jia-jun HUANG Zheng QIAO Hong-chao WEI Bo-xin ZHAO Yong-jie LI Jing-feng ZHAO Ji-bin 吴嘉俊;黄钲;乔红超;韦博鑫;赵永杰;李竟锋;赵吉宾

作者机构：State Key Laboratory of RoboticsShenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent ManufacturingChinese Academy of SciencesShenyang 110169China Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and EngineeringUniversity of Science and Technology of ChinaShenyang 110016China Faculty of Science and EngineeringUniversity of HullHull HU67RXUnited Kingdom Department of ChemistryTsinghua UniversityBeijing 100084China 

基　　金：Projects(51875558,51471176)supported by the National Natural Science Foundation of China Project(2017YFB1302802)supported by the National Key R&D Program of China 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2022年第29卷第10期

页      码：3346-3360页

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 *** experimental data of residual stress and microhardness were measured in the same *** residual stress and microhardness laws were investigated and *** 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 *** experimental data were divided as training-testing sets in *** energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as *** prediction performances with different network configuration of developed ANN models were compared and *** developed ANN model with network configuration of 4-7-6-2 showed the best predict *** predicted values showed a good agreement with the experimental *** addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were *** 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.

主 题 词：laser shock processing residual stress microhardness artificial neural network 

学科分类：12[管理学] 1201[管理学-管理科学与工程类] 081104[081104] 08[工学] 0805[工学-能源动力学] 080502[080502] 0835[0835] 0811[工学-水利类] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1007/s11771-022-5158-7

馆 藏 号：203115483...