Simulation based expert system to predict the deep drawing behaviour of tailor welded blanks

The forming behaviour of tailor welded blanks (TWB) is influenced by sheet thickness ratio, strength ratio and weld conditions in a synergistic fashion. In most of the cases, these parameters deteriorate the forming behaviour of TWB. It is necessary to predict suitable TWB conditions for achieving better stamped product made of welded blanks. This work primarily aims at developing an expert system based on artificial neural network (ANN) model to predict the deep drawing behaviour of TWBs made of steel grade base materials. The important deep drawing characteristics of TWB namely maximum draw depth and weld line profile are predicted within wide range of varied blank and weld conditions. The square cup deep drawing test is simulated in an elastic-plastic finite element code, PAM STAMP 2G (R), for generating the required output data for ANN training and validation. The predictions from ANN are encouraging with acceptable prediction errors.