Comparative analysis of the accuracy of neural network and analytical methods in modelling fatigue fracture of titanium alloy

Fatigue crack growth rate is modelled by neural network and compared with analytical models, namely, the Paris' law and polynomial log regressions of the second to fifth degree. To assess the accuracy of the modelling, the determination coefficient R², mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to assess the accuracy of the modelling. Results show that both approaches, in particular, the classical analytical models and the neural network, provide the high accuracy of approximation of the experimental data. At the same time, the neural network demonstrates an advantage in the region of high values of DK, where a nonlinear acceleration of crack growth is observed, reaching a coefficient of determination R² equal to 0.9994. The presented approach confirms the effectiveness of applying machine learning methods, in particular neural networks, in the problems of fracture mechanics and fatigue analysis.