Scattering of near normal incidence SH waves by sinusoidal and rough surfaces in 3-D: Comparison to the scalar wave approximation

Jarvis, A. J. C.; Cegla, Frederic

doi:10.1109/tuffc.2014.3017

Cited by 13 publications

(9 citation statements)

References 30 publications

(39 reference statements)

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“…The time domain signals are captured using the sampling time interval ∆t = 0.01 µs. This study is on the rough surface, and a surface with sinusoidal curves is used to model the rough surface [7,28]. Amplitude and wavelength of the curves are used to describe the arithmetic average heights and the horizontal parameter.…”

Section: Two-dimensional Rough Surface Model Setupmentioning

confidence: 99%

“…The finite element model (FEM) was used to accurately solve the problem of elastic wave scattering [24][25][26][27]. This numerical method was used to measure the thickness distribution of corrosion surfaces [28,29] and to analyze rough crack scattering [30,31]. Many factors must be considered when ultrasonic testing for a crack scatterer is discussed, including incident frequency, transducer location, and the noise induced by backscatter of ultrasound from the microstructure.…”

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confidence: 99%

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Effect of Surface Roughness on Ultrasonic Testing of Back-Surface Micro-Cracks

Wang

Cui

et al. 2018

Applied Sciences

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Surface roughness is one of the main factors that affect the ultrasonic testing of micro-cracks. This article theoretically analyzes the relationship between the changes in the energy intensity of crack echo waves and roughness-modified transmission coefficients. A series of simulations are carried out using two-dimensional sinusoidal curves as rough surface. Then, parallel experiments are performed on sample surfaces with different arithmetic average heights (Ra). The signal amplitude ratio factor (SARF) is defined to assess the ultrasonic detection capacity for micro-cracks. Both finite element analysis and experimental results show that signal amplitude decreases with an increase in Ra, resulting in signal-to-noise ratio loss. Amplitude attenuation caused by the rough back surface is less than that caused by the rough front surface. It is difficult to identify the signal of micro-cracks with a depth less than 400 μm when the Ra of the front surface is larger than 15 μm. Cracks with depths of more than 200 μm can be distinguished when the back-surface roughness is less than 24 μm. Furthermore, the amplitude of the micro-crack signal increases slightly with variation in the horizontal parameter (Rsm). This study provides a valuable reference for the precision evaluation of micro-cracks using ultrasonic inspection.

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Section: Two-dimensional Rough Surface Model Setupmentioning

confidence: 99%

mentioning

confidence: 99%

Effect of Surface Roughness on Ultrasonic Testing of Back-Surface Micro-Cracks

Wang

Cui

et al. 2018

Applied Sciences

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“…However, in reality, there is a significant difference between a 1D rough surface and three-dimensional (3D) rough surface. Jarvis and Cegla [ 8 , 9 ] also reconstructed a 1D rough surface with a SCF-based method, but the scattering events were simulated by the distributed point source method (DPSM).…”

Section: Introductionmentioning

confidence: 99%

Finite Element Simulation of Ultrasonic Scattering by Rough Flaws with Multi-Scale Distortions

et al. 2022

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The roughness of a flaw’s surface significantly affects the scattering behavior of ultrasonic waves. It is vital to understand the impact of roughness on flaw echoes, especially when performing ultrasonic nondestructive inspection on safety-critical components. However, the current approach for creating rough flaw models fails to reconstruct complicated cracks with secondary cracks. Here, a multi-scale distortion method is developed to generate a rough flaw by using an optical microscope image of a real flaw. The finite element (FE) is then implemented to simulate the near-surface rough flaws in nickel-based bars, which are detected by an offsetting immersion transducer with mode-converted transverse waves. Numerical results show that the randomness and complexity of flaw echoes from rough flaws are exceptionally high. The gap between the maximum and minimum normalized amplitude values of flaw echoes from a rough crack with secondary cracks can reach 7.125 dB. Meanwhile, the maximum time of flight (TOF) is almost twice as large as the minimum TOF. Therefore, the present method can generate effective rough flaw models in terms of macroscopic rough geometry and microscopic rough surface. Moreover, the impact of the rough flaw surface on the flaw echoes goes beyond amplitude changes and may make flaw location challenging.

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“…Usually a simple algorithm such as the timing between consecutive peaks (peak to peak-P2P) is used to evaluate the travel time and hence the wall thickness (see figure 2). However, if the inner wall surface of the instrumented pipe changes shape, such as during some corrosion/errosion processes, then the ultrasonic wave packets can become distorted [10,11]. This can lead to substantial changes/errors in the estimated wall thickness.…”

Section: Introductionmentioning

confidence: 99%

The effect of corrosion induced surface morphology changes on ultrasonically monitored corrosion rates

Gajdacsi

Cegla

2016

Smart Mater. Struct.

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Corrosion rates obtained by very frequent (daily) measurements with permanently installed ultrasonic sensors have been shown to be highly inaccurate when changes in surface morphology lead to ultrasonic signal distortion. In this paper the accuracy of ultrasonically estimated corrosion rates (mean wall thickness loss) by means of standard signal processing methods (peak to peak-P2P, first arrival-FA, cross correlation-XC) was investigated and a novel thickness extraction algorithm (adaptive cross-correlation-AXC) is presented. All of the algorithms were tested on simulated ultrasonic data that was obtained by modelling the surface geometry evolution coupled with a fast ultrasonic signal simulator based on the distributed point source method. The performance of each algorithm could then be determined by comparing the actual known mean thickness losses of the simulated surfaces to the values that each algorithm returned. The results showed that AXC is the best of the investigated processing algorithms. For spatially random thickness loss 90% of AXC estimated thickness trends were within −10 to +25% of the actual mean loss rate (e.g. 0.75-1.1 mm year −1 would be measured for a 1 mm year −1 actual mean loss rate). The other algorithms (P2P, FA, XC) exhibited error distributions that were 5-10 times larger. All algorithms performed worse in scenarios where wall loss was not distributed randomly in space (spatially correlated thickness loss occured) and where the overall rms of the surface was either growing or declining. However, on these surfaces AXC also outperformed the other algorithms and showed almost an order of magnitude improvement compared to them.

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Scattering of near normal incidence SH waves by sinusoidal and rough surfaces in 3-D: Comparison to the scalar wave approximation

Cited by 13 publications

References 30 publications

Effect of Surface Roughness on Ultrasonic Testing of Back-Surface Micro-Cracks

Effect of Surface Roughness on Ultrasonic Testing of Back-Surface Micro-Cracks

Finite Element Simulation of Ultrasonic Scattering by Rough Flaws with Multi-Scale Distortions

The effect of corrosion induced surface morphology changes on ultrasonically monitored corrosion rates

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