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 degrees, 19 degrees, 45 degrees, 71 degrees and 90 degrees. 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. (C) 2001 Elsevier Science Ltd. All rights reserved.