Investigating the flexural behavior of nanomodified multi-delaminated composites using acoustic emission technique

•The effect of incorporating different types of nanoparticles including carbon nanotubes and nanosilica on the flexural behavior of the multi-delaminated composites are investigated using the acoustic emission technique.•Wavelet packet transform and fast Fourier transform were used to quantify damage mechanisms in the composite samples.•Using genetic K-means and hierarchical techniques, acoustic data are classified based on the peak-frequency and amplitude.•According to the results obtained from the wavelet packed transform method, matrix cracking was determined as the dominant failure mechanism in all samples.•The hierarchical method was found more accurate in the classification of acoustic emission signals in comparison with than genetic k-means method. The formation of multiple delaminations is a frequently observed damage mechanism in composite materials, exerting a more pronounced influence on their strength properties compared to single delaminations. To tackle this issue, the incorporation of nanoparticles has been investigated as a means to enhance composite materials. This study aims to examine the effects of nano-additives, specifically carbon nanotubes and nanosilica, on the flexural behavior of glass/epoxy composites containing multiple embedded delaminations. The acoustic emission technique is employed to gain deeper insights into the damage mechanisms associated with flexural failure. Artificial delaminations of varying sizes, arranged in a triangular pattern, were introduced into four interlayers of a [(0/90)2]s oriented glass/epoxy composite. The findings reveal a notable reduction in flexural properties due to the presence of multiple delaminations. However, the addition of nanoparticles demonstrates a significant improvement in the flexural behavior of the multi-delaminated specimens. The most substantial enhancement is observed in the composite incorporating 0.3 wt% nanosilica + 0.5 wt% carbon nanotubes. Furthermore, genetic K-means and hierarchical clustering techniques are employed to classify different damage mechanisms based on the peak frequency and amplitude of the acoustic emission signals. The results indicate that the hierarchical clustering method outperforms the genetic K-means method in accurately clustering the acoustic emission signals. Moreover, the incorporation of nanoparticles' impact on the occurrence of distinct damage mechanisms is evaluated through the analysis of acoustic signals using Wavelet Packet Transform. By