Abstract
The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.
Highlights
Today, many kinds of materials are used for construction infrastructure, aviation and ocean sailing
This paper presents an analytical modeling method for acoustic emission (AE) caused by fatigue crack This paper presents an analytical modeling method for AE caused by fatigue crack growth in a thin plate using a piezoelectric sensor
Assuming the existence of type I fatigue cracks in an isotropic thin metal plate, the dynamic analytical expression of Lamb wave cracks in an isotropic thinismetal plate, the dynamic analytical expression of basis, Lamb the wave propagation in the plate derived by using the reciprocity theorem
Summary
Many kinds of materials are used for construction infrastructure, aviation and ocean sailing. Applying the technical conditions of continuous monitoring to the security of these infrastructures is a challenge [1]. AE technology has shown great advantages in monitoring large structures, and it allows effective health monitoring and life prediction of materials. It is important to detect damage in the early stages to prevent catastrophes from occurring [2]. In terms of material life prediction, Roberts et al linked the AE count rate with crack growth [5] in an effort to predict the material’s remaining life. Similar methods have been used to link observed AE data trends with fracture [6] and crack growth [7] in metallic materials
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