Reverse Engineering of Laser Welding Process Parameters for Ti6Al4V Alloy Based on Machine Learning

The mechanical performance of laser-welded Ti6Al4V alloy joints is governed by multiple process parameters with complex interplay, leading to nonlinear correlations, that complicate the quest for optimal parameters. In this paper, a reverse engineering model for process parameters was developed using backpropagation (BP) neural networks, targeting mechanical properties as the optimization objective for inverse parameter design. The BP neural network was enhanced via differential evolution tuning, achieving significant improvements in both mechanical property prediction and process parameter inversion. The prediction model demonstrated a relative error of approximately 3%, whereas the inverse model exhibited an error of about 6% under varying process conditions. A novel hybrid BP-WC model was then proposed by fusing weight coefficients from both the prediction and inverse models. This model reduced the inverse error of process parameters to 3%, providing a robust framework for efficient parameter optimization in laser welding for Ti6Al4V alloy.