Abstract
Abstract Through feature-based acoustic emission (AE) analysis and supervised clustering, data acquired during monotonic loading can reveal damage initiation, development, and accumulation within a specimen. In this work, AE analysis using a supervised pattern recognition method is carried out on specimens with co-cured composite joints of single lap and single nested overlap configurations. Correlation with physical observations from other techniques suggests that the resulting clusters may be associated with specific damage modes and failure mechanisms.
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