NDE OF COMPOSITES USING LEAKY LAMB WAVES (LLW)
Source abstract
Abstract
Abstract The leaky Lamb wave (LLW) technique is approaching a maturity level that is making it an attractive quantitative NDE tool for composites and bonded joints. Since it was first observed in 1982, the phenomenon has been studied extensively, particularly in composite materials. The wave is induced by oblique insonification using a pitch-catch arrangement and the resulting modes are acquired by identifying minima in the rellected spectra. These modes are documented in the form of dispersion curves, which are evaluated in relation to analytical data. The wave behavior in multi-orientation laminates has been well documented and corroborated experimentally with high accuracy. The sensitivity of the wave to the elastic constants of the material and to the boundary conditions led to the capability to measure the elastic properties of bonded joints. Recently, the authors significantly enhanced the experimental capability that is associated with the LLW technique by increasing the speed of the data acquisition and the number of modes that can be identified. This capability enables greater accuracy of the data inversion as well as improves the flaw characterization. In spite of the theoretical and experimental progress, methods that employ oblique insonification of composites are still not being applied as standard industrial NDE. The authors investigated the possible causes that are hampering the transition of the LLW to industrial application and identified several key issues. The analytical and experimental capabilities at the current stage are reviewed and the issues that are affecting the transition of the technique to practical use are described in this paper. keywords: Leaky Lamb Waves (LLW)NDECompositesStiffness constantsPlate wave modes
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Structural Health Monitoring Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (23)
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