Artificial neural network approach for mechanical properties prediction of TC4 titanium alloy treated by laser shock processing | |
Wu JJ(吴嘉俊)3,4,5; Huang Z(黄钲)3,4,5; Qiao HC(乔红超)4,5; Zhao YJ(赵永杰)2; Li JF(李竟锋)1; Zhao JB(赵吉宾)4,5 | |
刊名 | Optics and Laser Technology |
2021 | |
卷号 | 143页码:1-9 |
关键词 | Laser shock processing TC4 titanium alloy Residual stress Micro-hardness Artificial neural network |
ISSN号 | 0030-3992 |
产权排序 | 1 |
英文摘要 | Laser shock processing (LSP), which utilizes the stress effect induced by high-energy nanosecond pulse lasers to improve the mechanical properties and fatigue performance of metallic materials or alloys, is known as one of the most advanced surface modification techniques. In this work, a novel method based on artificial neural network (ANN) is applied to predict the residual stress and micro-hardness of TC4 titanium alloy treated by LSP. The experiment samples were treated by LSP with laser pulse energy of 3, 5, and 7 J and overlap rate of 10%, 30%, and 50%. Residual stress and micro-hardness were characterized by X-ray residual stress tester and Vickers micro-hardness tester. The ANN structure with four layers was employed, laser pulse energy, overlap rate and depth were set as the input parameters, while the residual stress and micro-hardness were set as the output parameters. The developed ANN model with the network configuration of 3 × 10 × 10 × 2 form a good correlation to predict residual stress and micro-hardness. The coefficient of determination R2, mean absolute error (MAE) and root mean squared error (RMSE) of testing data sets for residual stress and micro-hardness are 0.997 and 0.987, 7.226 and 2.632, and 9.956 and 3.321, respectively. It can be concluded that the ANN is a suitable method to predict the mechanical properties of materials treated by LSP when with limited experimental data. |
资助项目 | National Natural Science Foun-dation of China[51875558] ; NSFC-Liaoning Province United Foundation of China[U1608259] |
WOS关键词 | RESIDUAL-STRESSES ; MICROSTRUCTURE ; BEHAVIOR |
WOS研究方向 | Optics ; Physics |
语种 | 英语 |
WOS记录号 | WOS:000687054300003 |
资助机构 | National Natural Science Foundation of China (Grant No. 51875558) ; NSFC-Liaoning Province United Foundation of China (Grant No. U1608259) |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29315] |
专题 | 工艺装备与智能机器人研究室 |
通讯作者 | Wu JJ(吴嘉俊); Zhao JB(赵吉宾) |
作者单位 | 1.Tsinghua University, Beijing 100084, China 2.University of Hull, Hull, HU6 7RX, United Kingdom 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Wu JJ,Huang Z,Qiao HC,et al. Artificial neural network approach for mechanical properties prediction of TC4 titanium alloy treated by laser shock processing[J]. Optics and Laser Technology,2021,143:1-9. |
APA | Wu JJ,Huang Z,Qiao HC,Zhao YJ,Li JF,&Zhao JB.(2021).Artificial neural network approach for mechanical properties prediction of TC4 titanium alloy treated by laser shock processing.Optics and Laser Technology,143,1-9. |
MLA | Wu JJ,et al."Artificial neural network approach for mechanical properties prediction of TC4 titanium alloy treated by laser shock processing".Optics and Laser Technology 143(2021):1-9. |
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