Optimization of acoustic emission parameters to discriminate failure modes in glass–epoxy composite laminates using pattern recognition

In this article, to overcome the challenges encountered during the discrimination of various failure modes in post impacted/indented glass-fiber-reinforced plastic, techniques like pattern recognition method and advanced signal processing were employed. The significant acoustic emission parameters such as amplitude, rise time, counts, energy, duration, and peak frequency that are acquired during compression after impact test are considered as inputs to cluster validity index and for various clustering techniques such as k-means, fuzzy C-means, and Kohonen’s self-organizing map. The acoustic emission count–frequency and amplitude–frequency have no overlapping, whereas other combinations of acoustic emission parameters result in overlapping with four clusters. The clustering techniques are validated by discrete wavelet transform of acoustic emission signals. The discrete wavelet transform was performed on the clustered acoustic emission signals to identify the percentage of energy and frequency content of each level which correlates the different failure modes. The results infer that k-means, fuzzy C-means clustering, and Kohonen’s self-organizing map are 94.5%, 97.1%, and 98.6% reliability, respectively, clearly suggesting Kohonen’s self-organizing map as the most appropriate technique for the classification of acoustic emission signature