Full-field prediction of stress and fracture patterns in composites using deep learning and self-attention

An efficient surrogate modelling framework is proposed for full-field predictions of stresses and cracks in composite material microstructures. The framework comprises two sequential convolutional neural networks (CNNs), predicting the elastic stress fields and the local crack maps, respectively. Training and test data are created from high-resolution fracture simulations of randomly generated representative volume elements (RVEs), including geometric variabilities such as fibre volume fraction and porosity. This work shows that the inclusion of a self-attention layer within the network enables the model to capture relevant local and global features, which are important in determining the heterogeneous stress distribution and crack patterns. The performance of the trained CNN models is evaluated with unseen data. The CNN models speed up the full-field predictions by 3 ∼ 4 orders of magnitude compared to the physics-based model. The surrogate model's accuracy and efficiency are key enables for applications such as multiscale simulation, microstructure optimisation and uncertainty quantification.