Investigation of damage mechanisms of composite materials: Multivariable analysis based on temporal and wavelet features extracted from acoustic emission signals
A procedure for the investigation of damage development and time-tofailure mechanisms within composite materials based on the analysis of the signals of acoustic emission (AE) is presented. An unsupervised automatic classification is proposed for the clustering of the monitored AE events in order to identify the different damage mechanisms and the most critical damage sources in composite materials. Thus, pattern recognition analyses (fuzzy C-means clustering) associated with a principal component analysis are used for the classification. 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 polymer concretes. Furthermore, AE signals generated by heterogeneous materials are not stationary. Thus, timescale methods (continuous and discrete wavelet transforms) 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. They provide a better discrimination of damage mechanisms of composite materials such as cross-ply composites than some time-based descriptors.
KeywordsAcoustic Emission Damage Mechanism Discrete Wavelet Transform Acoustic Emission Signal Continuous Wavelet Transform
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