Towards In-Situ Failure Assessment: Deep Learning on DIC Results for Laminated Composites

Abstract

Predicting fracture load in laminated composites with stress raisers is challenging due to complex failure mechanisms such as delamination, fibre breakage, and matrix cracking, which are heavily influenced by fibre orientation, layup sequence, and notch geometry. This study aims to address this by developing a novel deep learning framework that leverages solely experimental strain field data from Digital Image Correlation (DIC) for accurate, in-situ predictions--bypassing the need for finite element simulations or empirical calibrations. Two complementary architectures are explored: a multi-layer perceptron (MLP) that processes numerical values of maximum principal strain from a targeted rectangular region ahead of the notch, enhanced by advanced feature selection (mutual information, Lasso, and SHAP) to focus on critical data points; and a convolutional neural network (CNN) trained on full-field strain images, bolstered by data augmentation to handle variability and prevent overfitting. Validated across 116 quasi-static tests encompassing 31 distinct configurations--including six layups (quasi-isotropic to highly anisotropic) with four off-axis angles for open-hole specimens, and one cross-ply layup with four off-axis and four on-axis notch orientations for U-notched specimens--the MLP and CNN achieve coefficients of determination (R^2) of 0.86 and 0.82, respectively. This framework captures a broad spectrum of damage modes and responses, from brittle fibre-dominated fracture to ductile delamination-driven failure, and due to its computational efficiency and reliance only on DIC measurements, the approach enables practical in-situ fracture load estimation.