Optimization for Loading Paths of Tube Hydroforming Using a Hybrid Method

Abstract

Tube hydroforming (THF) is an advanced technology with the advantages of lightweight and integrity, which can be used to manufacture hollow structural components. The process of THF is influenced by many factors, among which the matching relation between the internal pressure and axial feed, i.e., loading paths, is particularly important. In this article, a hybrid method is proposed to optimize loading paths of THF. Firstly, a three-layer back-propagation artificial neural network (BP-ANN) is built, and 200 samples from finite element (FE) simulations are applied to train and test the artificial neural network (ANN). Then genetic algorithm (GA) is adopted to search the optimal loading paths in the specified bounds of the design variables by using the trained ANN as the solver of the objective function and constraint functions. After 59 iterations, the optimal loading paths are obtained. Finally, the verified experiments are performed on the special hydroforming press. The results show that the proposed method can effectively search the optimal loading paths of THF and remarkably improve the quality of the final formed parts.