Ultrasonic reflector classification with autoregressive models

Houghton, J. R.; Shen, Peitao

doi:10.1007/bf01608221

Cited by 2 publications

(1 citation statement)

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“…The net result is a better understanding of the effects of damage morphology on the scattered ultrasonic signal and, therefore, on experimentally measured data. For example, autoregressive models have been developed that classify complex-shaped damage by extracting characteristic features of the received waveforms [11]. The classification is performed by comparing the extracted signal features, through any of a variety of comparison algorithms, with those of a library of reference models.…”

Section: Introductionmentioning

confidence: 99%

Experimental and simulated ultrasonic characterization of complex damage in fused silica

Martin

Chambers²,

Thomas³

2002

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The growth of a laser-induced, surface damage site in a fused silica window was monitored by the ultrasonic pulse-echo technique. The laser damage was grown using 12-ns pulses of 1.053-m wavelength light at a fluence of 27 J/cm 2 . The ultrasonic data were acquired after each pulse of the laser beam for 19 pulses. In addition, optical images of the surface and subsurface damage shape were recorded after each pulse of the laser. The ultrasonic signal amplitude exhibited variations with the damage size, which were attributed to the subsurface morphology of the damage site. A mechanism for the observed ultrasonic data based on the interaction of the ultrasound with cracks radiating from the damage site was tested using twodimensional numerical simulations. The simulated results exhibit qualitatively similar characteristics to the experimental data and demonstrate the usefulness of numerical simulation as an aid for ultrasonic signal interpretation. The observed sensitivity to subsurface morphology makes the ultrasonic methodology a promising tool for monitoring laser damage in large aperture laser optics used in fusion energy research.

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Section: Introductionmentioning

confidence: 99%