Journal
THEORETICAL AND APPLIED FRACTURE MECHANICS
Volume 117, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.tafmec.2021.103155
Keywords
Biomimetic; Laser shock peening; Residual fatigue life; Artificial neural network; Prediction
Categories
Funding
- National Natural Science Foundation of China [51705229, 52165017]
- Open Research Fund of State Key Laboratory of High Performance Complex Manufacturing, Central South University [Kfkt2020-02]
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This study investigates the effects of biomimetic laser shock peening on crack resistance ability of aluminum alloy aircraft skin. Results show that the treatment contributes to leaf-shaped residual stress distribution, reducing stress intensity factors and increasing residual fatigue life. An ANN linked with MCM is established to predict optimal structure with low relative error, saving design time and computation resource.
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.
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