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The paper presents signal and image processing algorithms to automatically detect delamination and disbond in composite plates from wavefield images obtained using a scanning laser Doppler vibrometer (LDV). Lamb waves are excited by a lead zirconate titanate transducer (PZT) mounted on the surface of a composite plate, and the out-of-plane velocity field is measured using an LDV. From the scanned time signals, wavefield images are constructed and processed to study the interaction of Lamb waves with hidden delaminations and disbonds. In particular, the frequency-wavenumber (f-k) domain filter and the Laplacian image filter are used to enhance the visibility of defects in the scanned images. Thereafter, a statistical cluster detection algorithm is used to identify the defect location and distinguish damaged specimens from undamaged ones.

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Lamb waves are being explored for structural health monitoring (SHM) due to their capability of detecting relatively small damage within reasonably large inspection areas. However, Lamb wave behavior is fairly complex, and therefore, various computational techniques, including finite element analysis (FEA), have been utilized to design appropriate SHM systems. Validation of these computational models is often based on a limited number of measurements made at discrete locations on the structure. For example, models of pitch-catch of Lamb waves may be validated by comparing predicted waveform time histories at a sensor to experimentally measured results. The use of laser Doppler vibrometer (LDV) measurements offers the potential to improve model validation. One-dimensional (1D) LDV scans provide detailed out-of-plane measurements over the entire scanned region, and checks at discrete sensor locations can still be performed. The use of three-dimensional (3D) laser vibrometer scans further expands the data available for correlation by providing in-and out-of-plane velocity components over the entire scanned region. This paper compares the use of 1D and 3D laser vibrometer data for qualitatively and quantitatively validating models of healthy metallic and composite plates.

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