Acoustic Emission Data Clustering for Analyzing Damage Mechanisms in Glass/Polyester Composites under Mode I Delamination

In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the discrimination of events due to different types of damage occurring during loading of composite materials. In the present work, a procedure for the investigation of local damage in composite materials based on the analysis of the signals of Acoustic Emission (AE) is presented. One of the remaining problems is the analysis of the AE signals in order to identify the most critical damage mechanisms. In this work, unsupervised pattern recognition analyses (fuzzyc-means clustering) associated with a principal component analysis are the tools that are used for the classification of the monitored AE events. 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 in order to improve the characterization and the discrimination of the damage mechanisms. The results show that there is a good fitness between clustering groups and damage mechanisms. Also, AE with clustering procedure are as effective tools that provide a better discrimination of damage mechanisms in glass/polyester composite materials.