Using an artificial neural network to predict the residual stress induced by laser shock processing

With the purpose of using the artificial neural network (ANN) method to predict the residual stresses induced by laser shock processing (LSP), the Ni-Cr-Fe-based precipitation-hardening superalloy GH4169 was selected as the experimental material in this work, and the experimental samples were treated by LSP with laser power densities of 4.24 G W / c m 2 , 7.07 G W / c m 2 , and 9.90 G W / c m 2 and overlap rates of 10%, 30%, and 50%. The depth-wise residual stresses of experimental samples prior to and after LSP were taken according to the x-ray diffraction sin 2 ψ method and electrolytic-polished layer by layer. The ANN model for residual stress prediction was established, and the laser power density, overlap rate, and depth were set as input parameters, while residual stress was set as the output parameter. The residual stresses of untreated samples and those treated with laser power densities of 4.24 G W / c m 2 and 9.90 G W / c m 2 were selected as the training sets, and the data of experimental samples treated with a laser power density of 7.07 G W / c m 2 were reserved as testing sets for validating the trained network. After LSP, beneficial stable compressive residual stresses were introduced in the material’s near surface, and the overall maximum compressive residual stresses were formed on the top surface (surface residual stress). Depending on the LSP process parameters, the surface residual stresses ranged from − 236 M P a to − 799 M P a , and the compressive residual stress depths of all treated samples were over 0.50 mm. According to the results obtained by ANN, the coefficient of determination R 2 of the training sets is 0.9948, which shows a good fitness for the training network. The R 2 of the testing sets is 0.9931, which is less than that of the training sets but still shows high accuracy. This work proves that the ANN method can be applied to predict the residual stress of metallic materials by LSP treatment with high accuracy and provides a guiding value for the optimization of the LSP process.