Strength prediction of carbon fibre-reinforced composites based on acoustic emission and damage pattern recognition

Currently, strength prediction based on damage pattern recognition of fibre-reinforced composite materials mainly relies on unsupervised clustering methods, which suffer from relatively limited evaluation metrics for clustering outcomes. This study proposes a strength prediction approach for fibre-reinforced composites based on acoustic emission and supervised damage pattern recognition. Firstly, three types of acoustic emission tests were designed to capture the characteristic acoustic emission signals of pure damage modes (fibre fracture, matrix cracking, interface debonding). These signal features were utilised to construct corresponding damage pattern datasets and train a supervised model, achieving a cross-validation accuracy of 92.94%. Subsequently, based on the trained model, the acoustic emission events collected during the tensile failure process of fibre-reinforced composite were distinguished, and the residual strength was predicted using a time series prediction model, yielding performance of MAE = 0.97, RMSE = 1.59, and R2 = 97.58%. Finally, the prediction results were compared with those derived from unsupervised clustering. The findings demonstrate that the supervised recognition-based approach delivers superior predictive outcomes. This indicates that the supervised identification method offers greater advantages in damage pattern recognition for fibre-reinforced composites. Consequently, this provides a novel approach for structural health monitoring of composite materials.