The Use of Neural Networks in the Prediction of the Fatigue Life of Different Composite Materials

Artificial Neural Networks (ANN) have recently been used in modeling the mechanical behavior of fiber-reinforced composite materials. The use of ANN in predicting fatigue failure in composites would be of great value if one could predict the failure of materials other than those used for training the network. This would allow developers of new materials to estimate in advance the fatigue properties of their material. In this work, experimental fatigue data obtained for certain fiber-reinforced composite materials is used to predict the cyclic behavior of a composite made of another material. The effect of the various mechanical properties in the training of the network is evaluated to obtain the most suitable combination of properties resulting in the best fatigue life prediction. An introduction to the use of Polynomial classifiers (PC) in the fatigue behavior is also considered.