Application of BPANN and regression for prediction of bowing defect in roll-forming of symmetric channel section

Abstract

In this paper both back-propagation artificial neural network (BPANN) and regression analysis are employed to predict the maximum downward deflection of the exit profile in roll-forming of symmetric channel section. To prepare a training set for BPANN, some finite element simulations were carried out. Sheet thickness, flange width, fold angle and friction coefficient were used as the input data and the maximum downward deflection as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the maximum downward deflection of the exit profile for any new given condition. Comparing FEA and BPANN results, an acceptable correlation was found. © Springer/ESAFORM 2008.