Fatigue life prediction of AA2524 thin plate strengthened using compound laser heating and laser shot peening method

AA2524 is a aluminum alloy based on damage tolerance design, and it is mainly used for the manufacturing of aircraft skins. Aircraft skins often occur fatigue failure due to harsh working conditions. To improve the fatigue resistance of aircraft skins and to accurately predict the fatigue life of AA2524 compact tension (CT) samples. The compound strengthening method of lsear heating (LH) and lsear shot peening (LSP) is proposed to improve the fatigue life of AA2524 in this paper. Considering the effect of stress ratio (R), residual stress (RS), stress intensity factor range (Delta K), and maximum stress intensity factor (Kmax) on the fatigue crack growth rate (FCGR), the fatigue life of CT samples was predicted applying artificial neural networks, support vector regression models. The results show that laser heating and laser shot peening can obviously improve the fatigue life by 30.703% at R = 0.1 and 4.701% at R = 0.5, compared with base materials. The neural networks have better fatigue life prediction ability, and the fit coefficients for the neural networks predicting fatigue life reach 0.97, which is better than that of Walker model (0.91). The prediction value for ANN model is 75,462 cycles, the magnitude for SVR model is 78,234 cycles, and the experimental value is 74,204 cycles. The deviation are 1.7% and 5.43%, respectively. Meanwhile, the effect of R on the FCGR of AA2524 is closely associated with the crack closure effect.