Monitoring of damage mechanisms in sandwich composite materials using acoustic emission

This work presents a procedure for investigating local damages created in sandwich composites. The proposed analysis is based on the processing of the recorded acoustic emission hits. Unsupervised pattern recognition analyses (k-means algorithm) associated to Principal Component Analysis (PCA) are the tools used for the classification of the detected acoustic emission events. A cluster analysis of acoustic emission data is achieved and the resulting clusters are correlated to the created damage mechanisms within the sandwich composite. The study is validated on the constituents of the sandwich material (resin, polyvinyl chloride foam and skins) taken separately before investigating the sandwich composite during a three-point bending test. In addition to the advantages given by the use of the multivariable data analysis, results show the impact of the transducers positioning on the analysis of the involved damage mechanisms. An optimized positioning is proposed in order to improve the detection of the acoustic emission activity.