Damage characterization of polymer-based composite materials: Multivariable analysis and wavelet transform for clustering acoustic emission data

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 (fuzzy C-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. After being validated on model samples composed of unidirectional fiber-matrix composites, this method is applied to actual composites such as glass fiber/polyester cross-ply composites and sheet molding compound (SMC). Furthermore, AE signals generated by heterogeneous materials are not stationary. Thus, time-scale 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. Continuous and discrete wavelet transforms are applied on typical damage mechanisms AE signals of glass fiber/polyester composites such as matrix cracking, fiber-matrix debonding. Time-scale descriptors are defined from these wavelet analyses. They provide a better discrimination of damage mechanisms than some time-based descriptors.