Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process

A new in-process identification method of material properties and lubrication condition in the deep-drawing process of anisotropic sheet metals is proposed and applied to the adaptive process control of the blank holding force (BHF). The method is based on a combination model of artificial neural network (ANN) and elastoplastic theory. Three delegated 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 friction coefficient μ and the optimal BHF control path were then calculated from the theoretical model. Furthermore, the friction coefficient was monitored during the entire process, and a closed-loop control was applied to modify the BHF path corresponding to the frictional variation. Experimental results show that the artificial intelligence (AI) control 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 for the quick responsible control system with high flexibility.