Energy-based fatigue life prediction of fiberglass/epoxy composites using modular neural networks

The strain energy has successfully been used in the past as a fatigue failure criterion for unidirectional fiber reinforced laminae. This approach has the ability to unify the macro- and microscopic behavior and can allow for extending the failure criterion to incorporate the multiaxial case. In this work, the strain energy will be used as an input to the artificial neural network (ANN) to predict fatigue failure. The results obtained will be compared to those obtained using the maximum applied stress, the fiber orientation angle and the stress ratio as inputs to the ANN.