Flaw detection and characterization using Lamb wave tomography and pattern classification.

Lamb waves are often used in nondestructive evaluation (NDE) and structural health monitoring because their multi-modal properties allow for interaction with many types of flaws. By collecting Lamb wave measurements in an array of pitch-catch positions around a given area, Lamb wave tomography allows for the generation of a reconstructed image that accurately locates and sizes flaws. However, these images often cannot predict the severity of flaw when scattering effects dominate. Pattern classification routines provide an alternative means for processing ultrasonic waveforms in order to predict flaw severity. The dynamic wavelet fingerprint technique is used to generate feature vectors from these complex multi-mode ultrasonic signals. This combination of tomography and pattern classification allows for better prediction of flaw severity in plate-like samples with defects of varying depths. [ABSTRACT FROM AUTHOR]

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