Crossed investigation of damage in composites with embedded quantum resistive strain sensors (sQRS), acoustic emission (AE) and digital image correlation (DIC)

In a previous paper, we had evidenced that the memory effect of the resistance of embedded quantum resistive strain sensors (sQRS) could be used to quantitatively assess the damage accumulation in glass fibre reinforced polymers (GFRP). In this work, to comfort this finding and to better understand the mechanisms making sQRS able to monitor the composite's damage, three techniques have been combined to look for correlations during incremental cyclic tensile tests. Experiments were performed on composite samples of different typologies, all instrumented with sQRS in their core. The strain profile measured in the vicinity of defects such as a hole or a notch by digital image correlation (DIC), has been used to determine the strain profile to which the percolated carbon nanotube network based resistive sensors (sQRS) were exposed. Further on, acoustic emission (AE) counts were used to identify the strain level over which damages were recorded to interpret the events detected on the piezo-resistive trace of sQRS, i.e., matrix fracture, interface decohesion, fibres breakage. It is found that sQRS, through the variation of their apparent gauge factor GF, are able to detect strain concentration in their surroundings and to identify damage events in the composite, provided that the fracture behaviour has been previously analysed by AE. Finally, sQRS can keep the memory of the damage accumulated in the composite as their initial resistance shift monitors the non-reversible events associated to damage. Their implementation in composite structures should offer interesting prospects to secure their use by helping to locate stress/strain singularities and potentially anticipate the complete failure.