Application of neural network and FEM for metal forming processes

This paper proposes a new technique to apply the artificial neural network in metal forming processes. A three-layer neural network is used and a back propagation algorithm is employed to train the network. It is determined by applying the ability of function approximation of the neural network to the initial billets which satisfy the minimum of incomplete filling in the die cavity. The die geometry for cylindrical pulley is designed to satisfy the design conditions of the final product. The proposed schemes have been successfully adapted to find the initial billet size for axisymmetric rib-web product and to design the die geometry for cylindrical pulley. The neural network may reduce the number of finite element simulation for designing the die of forging products and further it is usefully applied to multi-stage process planning.