An expert system for predicting the deep drawing behavior of tailor welded blanks
The forming behavior of tailor welded blanks (TWB) is influenced by thickness ratio, strength ratio, and weld conditions in a synergistic fashion. In most of the cases, these parameters deteriorate the forming behavior of TWB. It is necessary to predict suitable TWB conditions for achieving better-stamped product made of welded blanks. This is practically difficult and resource intensive, requiring lot of simulations or experiments to be performed under varied base material and weld conditions. Automotive sheet part designers will be greatly benefited if an expert system is available that can deliver forming behavior of TWB for varied weld and blank conditions. This work primarily aims at developing an expert system using artificial neural network (ANN) model to predict the deep drawing behavior of welded blanks made of steel grade and aluminium alloy base materials. The important deep drawing characteristics of TWB are predicted within chosen range of varied blank and weld conditions. Through out the work, PAM STAMP 2G finite element (FE) code is used to simulate the forming behavior and to generate output data required for training the ANN. Predicted results from ANN model are compared and validated with FE simulation for two different intermediate TWB conditions. It is observed that the results obtained from ANN based expert system are encouraging with acceptable prediction errors.
expert system deep drawing tailor welded blanks artificial neural network