Residual Strength and Corrosion Rate Predictions of Aging Aircraft Panels: Neural Network Study

The strength of an aging aircraft component deteriorates mainly as a result of corrosion and fatigue damage, among other failure mechanisms. Corrosion causes loss of material thickness, and the fatigue reduces the strength asa resultofdamageatmultiplesitesin aging aircraft. Thispaperpresentsa neural network study of both residual strength and corrosion of aging aircraft panels. Theresults obtained from theneural network model are compared to the analytical and experimental data available in the literature. The results obtained indicate that the neural network model predictions are in good agreement with the experimental data. To demonstrate the utility of neural network, several simulations are carried out to predict the trends with varying input parameters. Nomenclature a1 = MSD crack length measured from center of hole a2 = half-crack length of lead crack b = ligament length d = average diameter of the hole L = average MSD crack length measured from hole edge n = number of holes with MSD cracks Pc = critical load R = hole radius Dd=2 t = specimen thickness W = specimen width ± = hole spacing