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Vacuum assisted resin infusion (VARI) has attracted more and more attentions in the aerospace field and the monitoring of the process variables has been one of the milestones for most aerospace companies. This study focuses on monitoring the flow front and curing degree of the resin in‐suit using a piezoelectric (PZT) sensor network. In the resin transfer stage, the propagation characteristics of Lamb waves were extracted from different paths to characterize the resin flow front, and the quadratic regression models of three variables were established. In the resin cure stage, the leaky Lamb waves and electromechanical impedance based on PZT sensors were simultaneously adopted to monitor the degree of cure of the resin globally and locally, respectively. The differential scanning calorimeter analysis was carried to see the compatibility among different methods for the curing degree. Experimental results demonstrated that it was feasible to monitor the flow front and curing degree of resin in VARI process using Lamb waves and electromechanical impedance based on a multifunctional piezoelectric sensor network.

The electromechanical impedance (EMI) technique is one of common used methods for damage diagnosis. However, the mechanism of EMI-based damage diagnosing is usually complicated and difficult to be comprehensively described. To solve this problem to a certain extent, a novel EMI model for describing the interaction between piezoelectric transducer (PZT) and host structure is developed in this paper. The developed EMI model has advantages of simplicity in formula and excellent precision in signatures prediction. An efficaciously equivalent SMD (Spring-Mass-Damping) system is adopted to obtain the local structural mechanical impedance (LSMI) and to calculate the equivalent parameters ( ) in the vicinity of PZT. To verify the effectiveness of the novel model, a series of experiments are separately conducted on the specimen under healthy condition and damage condition. Through comparing the predicted curves with the experimental ones, the effectiveness of the developed model is demonstrated when used for predicting signatures, especially for resistance signatures. The developed EMI model has shown the potential application in the field of structural health monitoring and damage diagnosis.