Tensile Deformation Damage and Clustering Analysis of Acoustic Emission Signals in Three-Dimensional Woven Composites

Abstract

To study the deformation and damage behaviors of three-dimensional (3D) woven composites, uniaxial tensile tests of the composites were conducted and real-time acoustic emission (AE) signals and speckle images were simultaneously obtained. A k-means clustering algorithm combined with principal component analysis (PCA) was used to analyze the AE signals, and the deformation fields on the surface of composite specimens were measured by the digital image correlation (DIC) method. It was found that the corresponding AE signals were divided into three typical clusters. On the basis of the frequency range of each type, the relationships between the resulting clusters and damage mechanisms were established. The results showed that different damage mechanisms in 3D woven composites are well determined by the frequency rather than the peak amplitude. The characteristics of AE signals such as the high frequency, the medium frequency, and most of the low frequency are associated with fiber damage, fiber/matrix debonding, and matrix cracking, respectively. The change in the displacement distribution can effectively enable visual inspection of damage accumulation in composites.