Springback Effect Prediction in Wipe Bending Process of Sheet Metal: a Ga-ann Approach

Sheet metal is one of the most important semi-finished products used in automobiles, domestic appliances, aircraft and other familiar products. Therefore sheet metal forming technology is an important engineering discipline in the area of mechanical engineering. In sheet metal forming, wipe bending process plays a major role in which the sheet metal tries to return to its original shape after release of the load by a punch, due to the elastic stresses. This phenomenon is called as springback and the angle between the target bend and the original after elastic release is called sprinback angle. Springback angle prediction is essential while engaging with such wipe bending processes. State of the art methods use Artificial Neural Network with conventional configuration for the prediction of springback angle. To improve the prediction efficacy, this paper exploits Genetic Algorithm to provide appropriate training to Artificial Neural Network. The experimental investigations show that the proposed ANN approach predicts more precisely compared to the conventional ANN approach.