Corrosion fatigue life prediction method of aluminum alloys based on back-propagation neural network optimized by Improved Grey Wolf optimization algorithm

In order to improve the accuracy of the corrosion fatigue life prediction model for the 7050 aluminum alloy, this study presents a corrosion fatigue life prediction model based on back-propagation (BP) neural network optimized by improved grey wolf optimization (IGWO) algorithm that takes into account different sampling orientations and stress magnitudes. The model determines the mapping relationship between corrosion fatigue life, sample direction, and stress level. It also compares the improved grey wolf optimization-backpropagation (IGWO-BP) prediction error with three other models: BP, particle swarm optimization-back propagation (PSO-BP), and genetic algorithm-backpropagation (GA-BP). The IGWO-BP model's performance measures are as follows: a determination coefficient (R²) of 0.9740, a mean absolute error (MAE) of 0.0479, a root mean square error (RMSE) of 0.0596, and a mean absolute percentage error (MAPE) of 0.9443%. For both the training and test sets, the predicted results are within an error margin of 1.5 times. When compared with the conventional BP neural network, the R² of IGWO-BP is increased by 5.69%, and the RMSE, MAE, and MAPE of IGWO-BP are the smallest, reduced by 26.24, 26.19, and 26.72%, respectively.