Artificial neural network analysis of springback in V bending

Abstract

The aim of study is to define the springback angle with minimum error using the best reliable ANN training algorithm. Training and test data were obtained from experimental studies. Materials, bending angle and r/t have been used as the input layer; springback angle has also been used as the output layer. For testing data, Root Mean Squared-Error (RMSE), the fraction of variance (R2) and Mean Absolute Percentage Error (MAPE) were found to be 0.003, 0.9999 and 0.0831%, respectively. With these results, we believe that the ANN can be used for prediction of analysis of springback as an appropriate method in V bending. © 2008 Asian Network for Scientific Information.