Predicting the deep drawing process of TRIP steel grades using multilayer perceptron artificial neural networks

Abstract

TRIP (Transformation Induced Plasticity) steels belong to the group of ad-vanced high-strength steels. Their main advantage is their excellent strength combined with high ductility, which makes them ideal for deep drawing pro-cesses. The forming of TRIP steels in the deep drawing process enables the pro-duction of a thin-walled final product with superior mechanical properties. For this reason, this study presents comprehensive research into the deep drawing of cylindrical cups made from TRIP steel. The research focuses on three main aspects of the deep drawing process, namely the sheet metal thinning, the max-imum force value and the ear height as a result of the anisotropic material be-haviour. Artificial neural networks (ANNs) were built to predict all the men-tioned output parameters of the part or the process itself. The ANNs were trained using data obtained from a sufficient number of simulations based on the finite element method (FEM). The ANN models were developed based on variable material properties, including anisotropic parameters, blank holding force, blank diameter, and friction coefficient. A good agreement between sim-ulation, ANN and experimental results is evident.