The use of neural networks for the prediction of fatigue lives of composite materials

Constant-stress fatigue data for five carbon-fibre-reinforced plastics and one glass-reinforced plastic laminate have been used to evaluate possible artificial neural network architectures for the prediction of fatigue lives and to develop network training methods. It has been found that artificial neural networks can be trained to model constant-stress fatigue behaviour at least as well as other current life-prediction methods and can provide accurate (and conservative) representations of the stress/R-ratio/median-life surfaces for carbon-fibre composites from quite small experimental data-bases. Although their predictive ability for minimum life is less satisfactory than that for median life, and is non-conservative, the procedures developed in this work could nevertheless be used in design with little further modification. Some success has been achieved in modelling fatigue under block-loading conditions, but this problem is more difficult and requires much more effort before ANNs could be used with confidence for variable-stress conditions.