Damage progression monitoring using self-sensing capability and acoustic emission on glass fiber / epoxy composites and damage classification through principal component analysis

In this study, the synergistic combination of acoustic emission (AE) and self-sensing capability provided by the integration of carbon nanotube (CNT) networks was used to a better damage progression monitoring of glass fiber epoxy composites under flexural loading. The specimens were prepared with different stacking sequences ([06], [04], [0/90]S, and [90/0]S) using the vacuum assisted resin infusion procedure. The acoustic signals recorded during the tests were analyzed by a classification methodology which consists of the k-means method and principal component analysis (PCA), this analysis allowed the identification of the various damage mechanisms such as matrix cracking, fiber/matrix debonding, delamination and fiber breakage. The electromechanical response, through the change in electrical resistance signal, and some AE parameters such as the cumulative energy and/or strength cumulative were able to capture the occurrence of specific failure events during the composites damage evolution. Damage evaluation of failed specimens by means of scanning electron microscopy was performed, and it exhibited a less damage severity on specimens with CNT due to the reinforcement effect. This work demonstrates the complementarity of both non-destructive inspection techniques for monitoring damage progression in composite structures.