Damage characterisation of GFRP composites based on clustering acoustic emission events utilizing single-failure-cause tests as reference

A new method to identify causes of fracture in composites based on acoustic emission (AE) and clusterization of AE data based on reference datasets is presented within the manuscript. Acoustic Emission (AE) is a widely used non-destructive method for fracture analysis, but data due to their multidimensionality are not easy to analyze especially if the acoustic events appear simultaneously and have similar parameters even if they are an effect of different failure mechanisms. In this research, we utilize an unsupervised learning algorithm besides the simplest K-means, through fuzzy c-means to Gaussian Mixture Model (GMM) and spectral clustering to investigate the dataset obtained from the three-point bending test manufactured by us composite. The analysis is preceded by data curation, feature determination (Laplacian score) and the best number of cluster investigations (DB index, Caliński-Harabasz score, and Silhouette method) To enable interpretation of the clustering we run an additional three groups of tests covering fibre breakage (two methods), resin fracture (in tension and in compression) and delamination (DCB test) creating reference datasets. These datasets were statistically analyzed and kernel density estimators were generated for each AE feature as well as amplitude-frequency characteristics. Clusters obtained for the main dataset were then assigned to particular causes of failure by comparing them with the reference dataset. It was found that clusters generated using spectral clustering were the most realistic ones, as it was possible to assign the cause of failure to them.