Chapter . Development of an artificial neural network for defects prediction in metal forming

V. Corona, A. Maniscalco, R. Di Lorenzo

Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale Università di Palermo, Viale delle Scienze 90128 Palermo, Italy

Abstract

In bulk forming processes the prediction of ductile fractures and flow defects is a very important task. Generally the ductile fracture criteria are utilized for the estimation of damage accumulated by the material during the deformation. The principal defect of this approach is that each criterion gives a good result for some processes, instead not a good performance is obtained in relation to other processes. Thus, a considerable advantage is obtained by the implementation ...

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