The utilization of signal processing techniques in nondestructive testing, especially in ultrasonics, is widespread. Signal averaging, matched filtering, frequency spectrum analysis, neural nets, and autoregressive analysis have all been used to analyze ultrasonic signals. The Wavelet Transform (WT) is the most recent technique for processing signals with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past decade. In this work, the Wavelet Transform is utilized to improve ultrasonic flaw detection in noisy signals as an alternative to the Split-Spectrum Processing (SSP) technique. In SSP, the frequency spectrum of the signal is split using overlapping Gaussian passband filters with different central frequencies and fixed absolute bandwidth. A similar approach is utilized in the WT, but in this case the relative bandwidth is constant, resulting in a filter bank with a self-adjusting window structure that can display the temporal variation of the signal's spectral components with varying resolutions. This property of the WT is extremely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is described and numerical results show good detection even for signal-to-noise ratios (SNR) of -15 dB. The improvement in detection was experimentally verified using steel samples with simulated flaws.

No abstract

The wavelet transform is applied to the analysis of ultrasonic waves for improved signal detection and analysis of the signals. In instances where the mother wavelet is well defined, the wavelet transform has relative insensitivity to noise and does not need windowing. Peak detection of ultrasonic pulses using the wavelet transform is described and results show good detection even when large white noise was added. The use of the wavelet transform to extract the frequency dispersion relation of the Lamb wave velocity is also described. The twodimensional wavelet transform allows for both time and frequency analysis, thus making it particularly suitable for dispersion studies. Experimental and numerical results show the superior performance of the wavelet transform signal processor.

The room temperature longitudinal and shear acoustic velocities in polycrystalline NaCI have been measured at static pressures in the range of 25-270 kbar (2.5-27.0 GPa). The measurements were made by ultrasonic interferometry in a variable lateral support Bridgeman anvil device. The velocity data are developed with the aid of the Decker equation of state. Previous measurements of acoustic velocities made in the range of 0-100 kbar are in agreement with the data. At higher pressures the shear mode velocity is approximately constant or slowly increasing with increasing pressure. This is in agreement with predictions made with next nearest neighbor interatomic force calculations. Nearest neighbor only and fourthorder finite strain theory calculations which use the assumption that the elastic parameter c, goes to zero at the 292-kbar phase transition are determined to be inaccurate near the transition. It is suggested that the ratio of the acoustic velocities of polycrystalline NaCI can be used in the future as a parameter for calibration of ultrahigh-pressure devices.

Equation of state of aluminum nitride (AlN) is determined from the ultrasonic wave velocity measurements of longitudinal and shear modes to 0.7 GPa. The equation of state obtained from the ultrasonic data is used in conjunction with the shock Hugoniot data on AlN to estimate its strength under plane shock wave compression. Further, a better understanding of the existing shock Hugoniot data above its transition stress from wurtzite to rock salt structure is realized through a comparison with the static high-pressure investigations pertaining to this phase transition in AlN.