ARTIFICIAL-INTELLIGENCE IDENTIFICATION OF PROCESS PARAMETERS AND ADAPTIVE-CONTROL SYSTEM FOR DEEP-DRAWING PROCESS

A new in-process identification method of material properties and lubr ication condition in the deep-drawing process of anisotropic sheet met als is proposed and applied to the adaptive process control of the bla nk holding force (BHF). The method is based on a combination model of artificial neural network (ANN) and elastoplastic theory. Three delega ted plastic deformation properties, i.e. n value, F value and plastic anisotropic coefficient r, were identified using the measured process information at the beginning of the process by means of ANN. The frict ion coefficient mu and the optimal BHF control path were then calculat ed from the theoretical model. Furthermore, the friction coefficient w as monitored during the entire process, and a closed-loop control was applied to modify the BHF path corresponding to the frictional variati on. Experimental results show that the artificial intelligence (AI) co ntrol system can cover a wide range of both materials and influential parameters, such as friction and ambient temperature automatically. It is confirmed that the newly developed system is a valid alternative f or the quick responsible control system with high flexibility. (C) 199 8 Elsevier Science S.A. All rights reserved.