Material model for composites using neural networks

Two different back propagation neural networks were developed to represent the nonlinear stress-strain behavior of graphite-epoxy laminates under monotonic and cyclic loadings. The NN predictions for both monotonic and cyclic loadings are in good agreement with the experimental data obtained from literature. Preliminary results support the use of a NN approach to composite material modeling. The network developed in this study aids in identifying important aspects of the stress-strain behavior, such as breaking stress and fracture stress. This approach can also be used to predict failure mechanisms.