Application of a hybrid GA-BP optimized neural network for springback estimation in sheet metal forming process

There is a small but important deviation in sheet metal bending between the component angle and tool angle after unloading because of springback, i.e. elastic deformation. Since springback is unavoidable, the precision and reliability of products and subsequent assembly operations are severely affected. As a result of this lack of robustness intelligent technologies have received much attention in a wide range of metal-forming applications. Developments in faster computation techniques have made artificial neural networks (ANNs) and genetic algorithms (GA), very popular choices in modelling of sophisticated phenomenon. The present work, in order to construct the estimation model of springback, intends to integrate ANN with a hybrid genetic algorithm-back propagation (GA-BP) training method to determine appropriately the weights of neural network, making up for the defects of back propagation (BP) algorithm. In this paper, based on the available Experiments, three automotive body alloys using a range of die radius, friction coefficients and controlled tensile forces in a draw-bend process are considered. By using the developed estimation model further study on the relation of springback and various process parameters are carried out.

کلیدواژه ها:

Springback ، Prediction ، Metal forming ، Neural network ، Genetic algorithm