A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites

Abstract

A data-driven model is trained to predict mixed-mode stress intensity factors (SIFs) of composites through an artificial neural network (ANN) method. The model is based on a database generated by combining the interaction integral and the extended finite element method (XFEM). To reduce the input dimensionality and improve predictive performance, feature engineering is performed on the input data, and principal component analysis is conducted. Hyperparameters of the model are adjusted by using K-fold cross-validation and Bayesian optimization algorithm (BOA) to enhance the adaptability and generalization of the model. To overcome the data scarcity issue, an active learning knowledge extraction framework is constructed, which allows for accurate knowledge extraction even with limited data. By utilizing data-driven models to solve mixed-mode SIFs, the computational cost and complexity are greatly reduced compared to numerical simulations, while computational stability and ability to deal with high-dimensional nonlinear problems are significantly improved.