Grain size evaluation with time-frequency ultrasonic backscatter

Bu, Yangguang; Liu, Xiling; Turner, Joseph A.; Song, Yong; Li, Xiongbing

doi:10.1016/j.ndteint.2020.102369

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…n represents the number of decomposition layers. The number of decomposition layers depends on the length of the original input signal [7] .…”

Section: Resultsmentioning

confidence: 99%

“…In fact, the essence of EMD decomposition of ultrasonic backscattering signals is to filter out the high and low frequency components unrelated to grain size, leaving only IMF2 components highly correlated to grain size. Referring to previous studies [7,10] , the spectral variance P( ) f of the IMF2 of the laser ultrasonic waveform obtained at multiple measurement points was used to evaluate the backscattering acoustic noise level of each sample,…”

Section: Resultsmentioning

confidence: 99%

“…By calculating the correlation between the power of backscattered signal and the grain size, the ultrasonic backscattering evaluation model of grain size was established based on empirical mode decomposition (EMD). Bu et al [7] established a time-frequency analysis model for the grain size of 304 stainless steel by inversion of ultrasonic backscattering signal based on the water immersion ultrasonic experiments. Bai et al [8] established the finite element ultrasonic backscattering model based on the reciprocity theorem, and deduced the general formula for calculating the backscattering coefficient in two-dimensional and threedimensional models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of laser ultrasound in the detection of metal microstructure

Wu,

Bai,

Wang

et al. 2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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“…n represents the number of decomposition layers. The number of decomposition layers depends on the length of the original input signal [7] .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of laser ultrasound in the detection of metal microstructure

Wu,

Bai,

Wang

et al. 2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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“…The windowing function attempts to reach the peak of the signal and emphasizes it at that instant, while the remaining section of the signal is suppressed by the same window function. Several kinds of window functions exist in the literature: consider Kaiser, Blackman, Gaussian, Nuttall, Chebyshev, and others [39,40]. In the current study, the Gaussian windowing function was applied to STFT analysis and can be defined as [41]…”

Section: Short-time Fourier Transform (Stft)mentioning

confidence: 99%

Ultrasonic Assessment of Thickness and Bonding Quality of Coating Layer Based on Short-Time Fourier Transform and Convolutional Neural Networks

et al. 2021

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Ultrasonic non-destructive analysis is a promising and effective method for the inspection of protective coating materials. Offshore coating exhibits a high attenuation rate of ultrasonic energy due to the absorption and ultrasonic pulse echo testing becomes difficult due to the small amplitude of the second echo from the back wall of the coating layer. In order to address these problems, an advanced ultrasonic signal analysis has been proposed. An ultrasonic delay line was applied due to the high attenuation of the coating layer. A short-time Fourier transform (STFT) of the waveform was implemented to measure the thickness and state of bonding of coating materials. The thickness of the coating material was estimated by the projection of the STFT into the time-domain. The bonding and debonding of the coating layers were distinguished using the ratio of the STFT magnitude peaks of the two subsequent wave echoes. In addition, the advantage of the STFT-based approach is that it can accurately and quickly estimate the time of flight (TOF) of a signal even at low signal-to-noise ratios. Finally, a convolutional neural network (CNN) was applied to automatically determine the bonding state of the coatings. The time–frequency representation of the waveform was used as the input to the CNN. The experimental results demonstrated that the proposed method automatically determines the bonding state of the coatings with high accuracy. The present approach is more efficient compared to the method of estimating bonding state using attenuation.

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“…Through theoretical and experimental studies, the interaction of ultrasonic (i.e., elastic) waves with solid media can be quantitatively linked to internal features. Past studies in polycrystalline materials have investigated the effect of pores [14][15][16], grain size [17] and morphology [18], texture [19][20][21], residual stress [22,23], and elastic constants [24]. For several decades, these studies have focused on metals generated using traditional manufacturing techniques.…”

Section: Introductionmentioning

confidence: 99%