Cluster analysis of acoustic emission signals and deformation measurement for delaminated glass fiber epoxy composites

Abstract Understanding the failure mechanisms of the buckling process of glass fiber composites based on acoustic emission (AE) signals is a challenging task. In this work, a method combining AE with digital image correlation (DIC) was used to monitor compressive buckling behaviors of delamination composites. The analysis of AE signals is based on the k-means algorithm and principal component analysis (PCA). According to PCA, three characteristic parameters of AE signals like amplitude, peak frequency, and RA value (rise time divided by amplitude), are selected for cluster analysis by k-means algorithm. The results show that the AE signals of the compression process can be divided into three clusters. The three clusters correspond to three kinds of damage modes such as matrix cracking, fiber/matrix debonding, delamination and fiber breakage. The characteristic frequency of each mode is found by cluster analysis. Besides, the size and position of delamination defects result in the reduction of mechanical properties of the glass fiber composites. The complementary technology combining AE with DIC is effective for damage monitoring of the composites. Clear changes of the displacement fields can accurately detect the damage location and degree of the specimen.