Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction

¹ Mechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah university, Fez, Morocco
² Industrial Techniques Laboratory, Sidi Mohamed Ben Abdellah university, Fez, Morocco

^* Iliass El Mrabti: iliass.elmrabti@usmba.ac.ma

Abstract

In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.