Identification of Failure Modes in Composites from Clustered Acoustic Emission Data Using Pattern Recognition and Wavelet Transformation

Acoustic emission (AE) is widely used to characterize damage occurring in composite materials: however, the discrimination between AE signatures due to different damage mechanisms is still an open issue. In this study, the various failure mechanisms in bidirectional glass/epoxy laminates subjected to uni-axial tension are identified using AE monitoring. AE data recorded during the tensile testing of a single-layer specimen are used to identify matrix cracking and fiber failure. In contrast, delamination signals are characterized using a two-layer specimen with a pre-induced defect, produced by artificially inserting a 10 mm wide Teflon tape in the middle portion of the two layers. Twelve-layer Glass fiber reinforced plastics laminates were also tested as a reference for the comparison of results. The procedure leading to signal discrimination involves a number of steps. First, Fuzzy C-means clustering associated with principal component analysis are used to discriminate between failure mechanisms, while parametric studies using AE count rate and cumulative counts allowed damage discrimination at various stages of loading. The two above methods led to AE waveform selection: on the selected waveforms, fast Fourier transform (FFT) enabled calculating the frequency content of each damage mechanism. Continuous wavelet transform (WT) allowed identifying frequency range and time history for failure modes in each signal, while noise content associated with the different failure modes is calculated and removed by discrete WT. Short time FFT (STFFT) finally highlighted the possible failure mechanism associated with each signal.