A hybrid approach to the development of a multilayer neural network for wear and fatigue prediction in metal forming

In this paper an approach to surface damage prediction is proposed for the case of metal forming. The method is mainly based on three fundamental stages: (a) the detection of a feasible physical model which is able to give some important understanding of the phenomenon, although with limited generality; (b) the extensive development of an organized experimental campaign, which is necessary to tune up the developed model; and (c) the organization of an efficient and intelligent way of data collecting. The three aspects of the research work have been integrated by means of a neural network which is trained by using data coming from the real plant, from the standard tribometers, and from the reference numerical model. In this sense, the neural network is indented as hybridized. Predictions are shown to be very close to the experimental data obtained in the production plant. The method is useful for minimizing the number of experiments in the process of materials and treatment selection, and in maintenance.