Resumen
Assessing the damage evolution in carbon-fiber-reinforced polymer (CFRP) composites is an intricate task due to their complex mechanical responses. The acoustic emission technique (AE) is a non-destructive evaluation tool that is based on the recording of sound waves generated inside the material as a consequence of the presence of active defects. Proper analysis of the recorded waves can be used for monitoring the damage evolution in many materials, including composites. The acoustic track associated with the entire loading history of the sample or the structures is usually followed by using some descriptors, such as the amplitude of the sound waves and the number of counts. In this study, the acoustic emission in CFRP single-lap shear joints was monitored by using a multiparameter approach based on the contemporary analysis of multiple features, such as the absolute signal level (ASL), initiation frequency, and reverberation frequency, to understand whether a proper combination of them can be adopted for a more robust description of the damage propagation in CFRP structures. For selecting the best features, principal component analysis (PCA) was used. The selected features were classified into different clusters using fuzzy c-means (FCM) data clustering for analyzing the damage modes.