Effects and optimization of biomimetic laser shock peening on residual fatigue life improvement of aluminum alloy used in aircraft skin

被引:7
作者
Song, Minjie [1 ,2 ]
Liu, Jiaming [3 ]
Chen, Hongnan [1 ]
Hu, Yun [1 ]
Shi, Zhixin [1 ]
Yin, Hongna [1 ]
Xia, Jiani [4 ]
Berto, Filippo [5 ]
Li, Ruiqing [6 ]
机构
[1] Nanchang Univ, Dept Mechatron Engn, Nanchang 330031, Jiangxi, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Peoples R China
[3] Xiamen Univ, Sch Aerosp Engn, Xiamen 361102, Peoples R China
[4] Wuhan Univ Technol, Sch Foreign Languages, Wuhan 430070, Peoples R China
[5] Norwegian Univ Sci & Technol, Dept Mech & Ind Engn, Trondheim, Norway
[6] Cent South Univ, Light Alloy Res Inst, Changsha 410083, Peoples R China
基金
中国国家自然科学基金;
关键词
Biomimetic; Laser shock peening; Residual fatigue life; Artificial neural network; Prediction; SCANNING PATTERNS; THERMAL FATIGUE; WEAR-RESISTANCE; STRESS; MICROSTRUCTURE;
D O I
10.1016/j.tafmec.2021.103155
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
This paper aims to study the effects of biomimetic laser shock peening (BLSP) on crack resistance ability of aluminum alloy aircraft skin. The extracted feature parameters obtained from cherry leaves obey normal distribution. Based on the extracted biomimetic parameters, a 3D finite element model (FEM) is developed to study the effects of BLSP treatment on the residual stress distribution, stress intensity factors and residual fatigue life of specimens. Results show that BLSP treatment contributes to form leaf-shaped residual stress distribution. Indepth residual stress field is divided into three layers of compressive residual stress at both surfaces and tensile stress in the middle. Since the compressive residual stress generated by BLSP balances the load applied on specimens, stress intensity factors of treated specimens are reduced effectively, greatly increasing residual fatigue life. The asymmetry of residual stress distribution facilitates crack turning to prolong the crack path. An artificial neural network (ANN) with three layers is established to predict the optimal structure by linking with MonteCarlo method (MCM). The relative error of the ANN-MCM prediction, merely 3.26%, indicates that the proposed method is able to employed to save design time and computation resource.
引用
收藏
页数:14
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