Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks

Fatigue behavior of unidirectional glass fiber/epoxy composite laminae under tension¯tension and tension¯compression loading is predicted using artificial neural networks (ANN). Stress-life experimental data were obtained for fiber orientation angles of 0°, 19°, 45°, 71° and 90°. These tests were performed under stress ratios of 0.5, 0 and -1. The feedforward network used, provided accurate modeling between the input parameters (maximum stress, R-ratio, fiber orientation angle) and the number of cycles to failure. Although a small number of experimental data points were used for training the neural network, the results obtained are comparable to other current fatigue life-prediction methods.