Predicting crack behavior in holed plates using deep learning

In this study, we introduce a methodology for predicting the behavior of cracks in holed plates. The foundation of our methodology lies in the generation of rich dataset finite element simulations. These simulations capture the complex mechanical responses exhibited by holed plates under varying initial geometries. Using this dataset as training input, we employ a multilayer perceptron deep learning model to discern the underlying relationships between the plate’s initial geometry and its subsequent crack behavior. Through meticulous data preprocessing and fine-tuning of the model architecture, the MLP undergoes rigorous training and validation to optimize its predictive capabilities. Mean squared errors are utilized to assess the accuracy and generalization capacity of the trained model. The results suggest that this model can serve as a powerful rapid predictive tool, capable of analyzing crack behavior in new instances of the plate’s geometry with remarkable efficiency and accuracy.