Experimental and Numerical Investigation of PZT Response in Composite Structures with Variable Degradation Levels

• DOI: 10.1007/s11665-019-04011-4
• Publication Date: 2019-04-10T20:28:28Z
• Authors: Vittorio Memmolo; Hassan Elahi; Marco Eugeni; Ernesto Monaco; Fabrizio Ricci; Michele Pasquali; Paolo Gaudenzi
• Keywords: aerospace structures; composites; damage detection; electromechanical impedance; numerical simulation; piezoelectric material; self-diagnosis; 0203 mechanical engineering; numerical simulation; aerospace structures; piezoelectric material; 02 engineering and technology; electromechanical impedance; aerospace structures; composites; damage detection; electromechanical impedance; numerical simulation; piezoelectric material; self-diagnosis; materials science (all); mechanics of materials; mechanical engineering; composites; self-diagnosis; damage detection

Commercial aerospace vehicles have been increasingly demanding to withstand harsh conditions with low-weight material, i.e., composites. Unfortunately, low-velocity impacts strongly affect their performance. Structural health monitoring with permanently attached sensors allows achieving cost-effective maintenance and tearing down knockdown factors. However, the degradation of transducers adopted for online detection of damage negatively affects the diagnosis. That deterioration is addressed in this work with the electromechanical impedance approach employed at relatively low ultrasonic frequencies. Several degradation conditions are investigated with experimental and numerical simulations. The results demonstrate how the self-diagnosis approach detects pure sensor failures without any structural dependence. However, self-detection of the mixed mode of failures is challenging due to the opposite effect that different types of failure return. Numerical simulations provide a spectral response in compliance with measurements. On top of that, numerical results demonstrate that the combination of different types of damage may induce missed detection. That is where a multi-parameter self-diagnosis approach may further improve the overall monitoring system.