Prediction of Deformed Configuration and Ductile Fracture for Simple Upsetting Using an Artificial Neural Network

This paper suggests a scheme for simultaneously accomplishing the prediction of fracture initiation and geometrical configuration of deformation in metal forming processes using an artificial neural network. A three-layer neural network is used and a back-propagation algorithm is adapted to train the network. The Cockcroft–Latham criterion is used to estimate whether fracture occurs during the deformation process. The geometrical configuration and the value of ductile fracture are measured by the finite-element method. The predictions of the neural network and the numerical results of simple upsetting are compared. The proposed scheme has predicted the geometrical configuration and fracture initiation successfully.