Abstract: The rate of thinning in incremental forming is a crucial indicator for assessing the quality of formed parts. In this study, we conducted experiments using a Box-Behnken design experimental scheme to analyze the impact of tool diameter (D), layer spacing (Δz), forming angle (α), and plate thickness (t) on the thinning rate. By obtaining an optimal combination of these parameters, we established a BP neural network model that correlates process parameters with thinning rate. The model was trained using a data training set and its accuracy in predicting the thinning rate for a test set was evaluated. To address the issue of high average error in the BP neural network model (6.42%), we employed particle swarm optimization (PSO) to optimize its parameters, resulting in a reduced prediction error of 2.24%. The PSO-BP neural network model effectively predicts the relationship between process parameters and thinning rate.