Research concerning the springback prediction in the bending operations

Nowadays firms are required to obtain high quality products in order to increase their competitiveness. The time required to obtain a new product is also essential to fight the concurrence. For manufacturers of bent parts, accurate prediction of the springback is very important. Therefore, this paper investigates the applicability of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the springback in the free cylindrical bending process of metallic sheets. The finite element method (FEM) was used to simulate the springback in the free cylindrical bending process and the results were used as training data for ANN and ANFIS. The finite element results were validated by comparison with experimental data. Statistic criteria were used to evaluate the performance of the developed ANN and ANFIS models. It was found that the predictions are in good agreement with the FEM data.