An artificial neural network modeling approach for short and long fatigue crack propagation

Abstract

Fatigue crack growth-based damage modeling approaches have received great interest due to its critical importance in the industry. However, a substantial deficiency of an explicit fatigue damage model to quantify the accurate fatigue crack growth behavior remains due to the complex fatigue crack growth behavior in different length scales. This complexity arises from the fact that fatigue crack growth (FCG) in different length scales depends on many damage controlling parameters. Machine learning-based fatigue damage modeling approaches have received noticeable attention for fatigue crack growth analysis due to their abilities to account for numerous damage parameters simultaneously. In the presented paper, a radial basis function artificial neural network (RBF-ANN) model has been developed to predict the FCG behavior, including the short and long crack regimes. The presented RBF-ANN model has been trained and verified by experimental data sets of Ti-6Al-4V titanium alloy, 2024-T3 and 7075-T6 aluminium alloys. The predictions showed that the RBF-ANN model has a good interpolation capability to predict the nonlinearity of both short and long crack growth behavior. However, the model shows poor extrapolation capability for accurate short crack growth predictions for cases that there are limited data sets in hand. The model effectiveness greatly depends on sufficient available input data.