Unsupervised acoustic emission data clustering for the analysis of damage mechanisms in glass/polyester composites

Abstract

In using acoustic emissions (AEs) for mechanical diagnostics, one major problem is the discrimination of events due to different types of damage occurring during loading of composite materials. Unsupervised pattern recognition analyses (fuzzy c-means clustering) associated with a principal component analysis (PCA) are the tools that are used for the classification of the monitored AE events. Composites at different layups are used with the acoustic emission technique. A cluster analysis of AE data is achieved and the resulting clusters are correlated to the damage mechanisms of the material under investigation. Time domain methods are used to determine new relevant descriptors to be introduced in the classification process to improve the characterization and the discrimination of the damage mechanisms. The results show that there is a good fit between clustering groups and damage mechanisms. Additionally, AE with a clustering procedure are effective tools that provide a better discrimination of damage mechanisms in glass/polyester composite materials.