Optimization of process parameters and prediction of thinning rate for double-sided incremental forming
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.
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