Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
794160 | Journal of Materials Processing Technology | 2007 | 5 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Wenjuan Liu, Qiang Liu, Feng Ruan, Zhiyong Liang, Hongyang Qiu,