Damage evaluation of adhesive composite joints using acoustic emission and self-sensing capability

An analysis of the damage evolution of glass fiber reinforced composite single lap joints (SLJ) subjected to tensile loads was performed by the combined use of acoustic emission (AE) and the piezoresistive effect of 3D-printed sensors embedded in the adhesive layer. The acoustic emission signals were evaluated through a classification methodology using the k -means method and principal component analysis (PCA). The AE signals were evaluated using a classification methodology based on k-means clustering and principal component analysis (PCA), allowing AE events with different characteristics to be grouped into distinct classes. Based on comparison with previously reported AE characteristics and complementary post-mortem observations. The conductive filament sensors embedded in the adhesive layer exhibited a measurable piezoresistive response that followed the mechanical loading of the adhesive joint under monotonic and short-term cyclic loading, as indicated by variation in electrical resistance. The damage assessment of the failed specimens was performed by scanning electron microscopy. The development of additive manufacturing techniques that enable the fabrication of customized sensors promotes potential applications for monitoring composite structures. The use of 3D printed piezoresistive sensors proposed in this work is an innovative method for structural health monitoring (SHM) of adhesive composite bonded components. In addition, the combination of AE and piezoresistivity techniques provides an alternative for the SHM of composite adhesively bonded joints.