Abstract
The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb wave theory and using this understanding for predictive modeling of AE signals. After a brief introduction, the paper discusses the principles of sensing acoustic signals by using PWAS. The derivation of a closed-form expression for PWAS response due to a stress wave is presented. The transformations happening to the AE signal according to the instrumentations we used for the fatigue crack AE experiment is also discussed. It is followed by a summary of the in situ AE experiments performed for recording fatigue crack growth AE and the results. Then, we present an analytical model of fatigue crack growth AE and a comparison with experimental results. The fatigue crack growth AE source was modeled analytically using the dipole moment concept. By using the source modeling concept, the analytical predictive modeling and simulation of the AE were performed using normal mode expansion (NME). The simulation results showed good agreement with experimental results. A strong presence of nondispersive S0 Lamb wave mode due to the fatigue crack growth event was observed in the simulation and experiment. Finally, the analytical method was verified using the finite element method. The paper ends with a summary and conclusions; suggestions for further work are also presented.
Highlights
Engineering structures are prone to various types of failures during their operation.The failure mechanism during their operation depends on the mechanism of the external loading, working conditions, the microstructural changes inside the structure, etc
This paper presented analytical predictive modeling of acoustic emission (AE) signals sensed using Piezoelectric wafer active sensors (PWASs) sensors
This paper presented analytical predictive modeling of AE signals sensed using PWAS sensors during a fatigue crack growth event
Summary
Engineering structures are prone to various types of failures during their operation. The AE technique has been used for fatigue crack growth damage detection and source localization. The AE signal signatures recorded by piezoelectric wafer active sensor (PWAS) transducers during a fatigue crack growth event in thin metallic plates were studied by Bhuiyan et al [40,41,42]. The present paper discusses the predictive modeling of the fatigue crack growth AE signals in thin sheet metals. The novelty of the present research is the derivation of a closed-form solution for predictive modeling of fatigue crack growth AE signals sensed using PWAS sensors in thin plates and its experimental validation. The paper derives the theoretical expression for predictive modeling of PWAS sensed AE signals due to a fatigue crack growth event in thin metallic plates, followed by a comparison with the experimental results. The final part of the article presents the summary and conclusions, and makes suggestions for further work
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