Insight into local defect resonance and its in-situ measurement based on flexible nano-composite sensors

The local defect resonance has been proven to be effective for damage detection, and the methods based on LDR for damage evaluation have demonstrated appealing capabilities of selective assessment and applicability to complex structures. To explain the generation of LDR, models based on vibration theories have been developed in which the defect boundaries are assumed to be fixed. Nevertheless, existing models can only give an accurate prediction of frequency for fundamental LDR mode owing to the deviation of their assumptions from the reality. In addition, the measurement of LDR is currently limited to noncontact technologies, hampering its application to in-situ and online monitoring of inspected structures. To overcome these deficiencies, an analytical model is proposed in this investigation to gain an insight into the generation of LDR from the perspective of wave reflection at defect boundaries and wave superposition. In this model, the interaction of guided waves with defect is scrutinized using a normal mode expansion method to obtain the phase shift of the reflected waves at the defect boundaries. On this basis, the formation of standing waves is analyzed to interpret the occurrence of the resonance of guided waves, whereby the relation of the LDR frequencies and the defect parameters can be obtained. On top of this analytical investigation, the sensing of the LDR using a novel nano-composite sensor is attempted. Flexible and lightweight, these sensors can form a dense sensing network, with which the LDR response in the inspected region can be perceived. Using the LDR-based method and nano-composite sensors, this approach can give a damage characterization in an in-situ and online manner for complex structures. Experimental validations of the proposed approach is performed in which delaminations in composite structures are identified and assessed using the LDR response perceived with the nano-composite sensors.