on acoustic emission for failure investigation in cfrp: pattern recognition and peak frequency analyses

this paper investigates failure in carbon fibre reinforced plastics cfrp using acoustic emission (ae). signals have been collected and post-processed for various test configurations: tension, compact tension (ct), compact compression (cc), double cantilever beam (dcb) and four-point bend end notched flexure (4-enf). the signals are analysed with three different pattern recognition algorithms: k-means, self organising map (som) combined with k-means and competitive neural network (cnn). the som combined with k-means appears as the most effective of the three algorithms. the results from the clustering analysis follow patterns found in the peak frequencies distribution. a detailed study of the frequency content of each test is then performed and the classification of several failure modes is achieved.