Research on the Optimization of Process Parameters of the Current-Assisted Flow Spinning for 30CrMnSiA Cup-Shaped Part with Different Thickness

Abstract

A multi-pass current-assisted flow spinning experiment was carried out for the cup-shaped part with different thicknesses. The thickness deviation At, straightness u, and roundness e of the spinning parts were selected as the evaluation indexes of forming quality, and the effects of process parameters such as current intensity, preheating time, feed ratio and thinning rate on the forming quality were studied. Based on the genetic algorithm and BP neural network (GA-BP) model, the quality prediction model for the current-assisted flow spinning process was established, and the forming process was optimized by the genetic algorithm (GA). The verification test results are close to the predicted values of the model, in which the maximum error between the predicted value and the test result is about 6%. The results show that the proposed method is useful for the optimization of the current-assisted forming flow spinning process.