Structural health monitoring of polymer-matrix composites (PMCs) using embedded piezoelectric transducers: experimental and numerical approaches

This doctoral thesis presents a comprehensive investigation combining experimental study and numerical modeling of smart Polymer-Matrix Composites (PMC) embedding ceramic and polymeric piezoelectric transducers. The primary objective was to enhance their intelligent functionality to establish experimental/numerical Structural Health Monitoring (SHM). The numerical model, using the Carrera Unified Formulation (CUF) developed at MUL2 in PoliTo, provides a robust foundation for analysis via plate and beam models of higher-order kinematics. The experimental phase was started with the self-heating tensile tests conducted on the composite specimens to estimate their fatigue limit through the conventional method (using the Infrared Thermography technique, IRT) and an innovative approach employing the electrical embedded capacitance variation. A detailed description of the experimental setup, including the specimen types and testing conditions, enhances the clarity of this crucial phase. Subsequently, the electrical capacitance variation under various tensile tests was studied and correlated with different non-destructive testing methods. A novel comparison between experimental and numerical capacitance variation was presented, suggesting further development in this area. In the same perspective, the static embedded capacitance variation was explored with a corresponding numerical study employing a 2-D plate model. The results demonstrated a compelling agreement with the few existing literature, validating the efficacy of the numerical approach. Lamb wave propagation analysis further contributes to the understanding of smart isotropic and composite structures. The investigation of the impact of the numerical model parameters on the obtained results enriched the interpretation of the findings, providing valuable insights for practical applications. Finally, the optimal transducer embedding position within the thickness, coupled with a thorough exploration of sensor size and thickness, was conducted both numerically and experimentally. The significance of these optimizations was clarified, emphasizing their potential contributions to advancing SHM capabilities. In totality, this work integrates experimental and numerical methodologies offering a novel perspective on smart PMC applications for SHM purposes. The insights gained from the self-heating tests, electrical capacitance variation, Lamb wave propagation, and optimal transducer embedding position and dimensions collectively contribute to a more comprehensive understanding of the conducted SHM task.