Springback prediction for sheet metal forming based on GA-ANN technology

Springback is a very important factor to influence the quality of sheet metal forming. Accurate prediction and controlling of springback is essential for the design of tools for sheet metal forming. In this paper, a technique based on artificial neural network (ANN) and genetic algorithm (GA) was proposed to solve the problem of springback. An improved genetic algorithm was used to optimize the weights of neural network. Based on production experiment, the prediction model of springback was developed by using the integrated neural network genetic algorithm. The results show that more accurate prediction of springback can be acquired with the GA-ANN model. It can be taken as a reference for sheet metal forming and tool design. (C) 2007 Elsevier B.V. All rights reserved.