In this study, graphene particles are introduced to the lead magnesium niobate-lead titanate (PMN-PT) and polyvinylidene fluoride (PVDF) to form a flexible ternary composite. The graphene concentration is rigorously designed and morphologically optimized, warranting good piezoelectric and dielectric properties. The piezoelectric and dielectric performances are greatly increased compared with the pure PVDF films. Then a theoretical model is formulated to quantitatively interpret the graphene effect on the permittivity performance and to provide guidelines for the optimization of graphene volume fraction. Moreover, a simple and cost-effective technique is designed to package the composite film as a large-area, lightweight and flexible transducer. Several confirmatory experiments and a proof-of-concept test are performed based on the proposed flexible piezoelectric transducers to validate the capability of the dynamic strain sensing. By comparing with the results from conventional strain gauges and ceramic piezoelectric wafers, it is verified that the proposed flexible transducer has proven responsivity and precision in responding to quasi-static strain, medium-frequency vibration, and ultrasound. The great potential of the developed transducer for a wide range of applications including structural health monitoring and human motion detection has been demonstrated.
Uncertainty in Non-Destructive Evaluation (NDE) arises from many sources, e.g., manufacturing variability, environmental noise, and inadequate measurement devices. The reliability of the NDE measurements is typically quantified by the probability of detection (POD). With the advent and technical developments of the simulation method and computer science, efforts have been devoted to generating and estimating the POD curve for Lamb wave damage detection. However, few studies have been reported on the POD evaluation considering model selection uncertainty. This paper presents a novel POD assessment method incorporating model selection uncertainty for Lamb wave damage detection. By treating the flaw quantification model as a discrete uncertain variable, a hierarchical probabilistic model for Lamb wave POD is formulated in the Bayesian framework. Uncertainties from the model choice, model parameters, and other variables can be explicitly incorporated using the proposed method. The Bayes factor is used to evaluate the performance of models. The posterior distributions of model parameters and the model fusion results are calculated through the Bayesian update using the reversible jump Markov chain Monte Carlo method. A fatigue problem with naturally developed cracks is used to demonstrate the proposed method.
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