Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15460
Title: Optimization for loading paths of tube hydroforming using a hybrid method
Authors: Zhang, Y
Chan, LC 
Wang, C
Wu, P
Keywords: Axial feed
BP-ANN
Constraint function
FEM
Finite element method
Genetic algorithm
Hydraulic press
Hydraulic system
Internal pressure
Loading paths
Manufacturing technology
Objective function
Optimum method
Processing parameters
Simulation
Tube hydroforming
Issue Date: 2009
Publisher: Taylor & Francis Inc
Source: Materials and manufacturing processes, 2009, v. 24, no. 6, p. 700-708 How to cite?
Journal: Materials and Manufacturing Processes 
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.
URI: http://hdl.handle.net/10397/15460
ISSN: 1042-6914
DOI: 10.1080/10426910902769392
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