An optimization strategy based on a metamodel applied for the prediction of the initial blank shape in a deep drawing process

The transformation of the sheet into a product without failure and excess of material in a deep drawing operation means that the initial blanks should be correctly designed. Therefore, the initial blank design is a critical step in deep drawing design procedure. Consequently, an easy approach for engineers in predicting the initial blank shape is necessary to reduce wastage in material and to overcome the large time consumed in the classical approaches. Thus, the aim of the present investigation is to propose an automatic procedure for the quick sheet metal forming optimization. In fact, a metamodel will be build based on artificial neural networks which will be coupled then with an optimization procedure in order to predict the initial blank shape in a rectangular cup deep drawing operation. The metamodel is built from the finite element simulations using ABAQUS commercial code. This procedure allows a significant reduce of the CPU time compared to classical optimization one. The results show that the desired shape is in good agreement with the one calculated using the optimized blank shape.