<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhishek T Dhumal</style></author><author><style face="normal" font="default" size="100%">Ganesh Narayanan, R</style></author><author><style face="normal" font="default" size="100%">Saravana Kumar, G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation based expert system to predict the deep drawing behaviour of tailor welded blanks</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Modelling, Identification and Control</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Deep drawing</style></keyword><keyword><style  face="normal" font="default" size="100%">expert systems</style></keyword><keyword><style  face="normal" font="default" size="100%">forming behaviour</style></keyword><keyword><style  face="normal" font="default" size="100%">maximum draw depth</style></keyword><keyword><style  face="normal" font="default" size="100%">modelling</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">steel</style></keyword><keyword><style  face="normal" font="default" size="100%">Tailor welded blanks</style></keyword><keyword><style  face="normal" font="default" size="100%">TWB</style></keyword><keyword><style  face="normal" font="default" size="100%">weld line profile</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">164 - 172</style></pages><abstract><style face="normal" font="default" size="100%">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®, generating the required output data for ANN training and validation. The predictions from ANN are encouraging with acceptable prediction errors.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>