Discrete Wavelet Transform based Denoising of TOFD Signals of Austenitic Stainless Steel Weld at Elevated Temperature

Lalithakumari, S.; Pandian, R.

doi:10.1134/s1061830919050073

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Actually, Peak detection is not accurate, leading to suboptimal straightening effects. Peak detection can identify the approximate position of peaks, but achieving a more precise straightening of lateral waves requires leveraging the correlation between multiple A-scan signals after applying discrete wavelet transform for denoising A-scan signals [9] .…”

Section: Lateral Wave Straighteningmentioning

confidence: 99%

Lateral wave removal of time-of-flight diffraction based on peak detection and correlation analysis

Yao,

Wang,

Tao

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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The Time of Flight Diffraction (TOFD) method stands as a highly efficient, cost-effective, and environmentally friendly approach for defect detection within the domain of ultrasonic testing. This method proves particularly efficacious in discerning weld seam defects within mechanical welded structures. The D-scan image assumes a pivotal role in detecting flaws within low-alloy thick plate butt welds. In D-scan images, a considerable portion of the data, under reasonable scanning depths, serves as background rather than conveying defect information. This background, primarily noise, acts as non-target interference and significantly disrupts defect identification. Particularly, when the probe detection angle approaches the surface, lateral waves emerge as relatively stable and continuously distributed noise in the image, characterized by trailing tails in time. This characteristic makes them susceptible to overlapping with signals from nearsurface defects, resulting in blind spots during detection. To enhance the clarity of D-scan images, this paper introduces an expeditious and potent technique for lateral wave straightening and removal. the proposed method relies on the peak detection to A-scan and correlation analysis of A-scan signals for the meticulous rectification of D-scan signals. Moreover, it incorporates an insightful examination of image energy distribution and one-dimensional wavelet transform to effectively remove lateral waves, facilitating the generation of clear and discernible images of weld seam defects.

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Section: Lateral Wave Straighteningmentioning

confidence: 99%

Lateral wave removal of time-of-flight diffraction based on peak detection and correlation analysis

Yao,

Wang,

Tao

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…This method's results showed that the SNR of the TOFD signals had improved. Different combinations of wavelets [12], decomposition levels, and thresholding were employed to develop an optimum denoising algorithm; the performances were then evaluated using the SNR calculations. To create the best denoising algorithm, various wavelet combinations [12], decomposition levels, and thresholding were used, then the performance was assessed using SNR estimates.…”

Section: Literature Review 21 Related Work On Tofdmentioning

confidence: 99%

“…Different combinations of wavelets [12], decomposition levels, and thresholding were employed to develop an optimum denoising algorithm; the performances were then evaluated using the SNR calculations. To create the best denoising algorithm, various wavelet combinations [12], decomposition levels, and thresholding were used, then the performance was assessed using SNR estimates. In the same year, wavelet transform for TOFD data was examined by the authors of [13] for the automatic positioning and sizing of flaw detection.…”

Section: Literature Review 21 Related Work On Tofdmentioning

confidence: 99%

“…The SVM classifier is reliable, promising, and performs well, even in the absence of training data. The classification of a lack of fusion and a lack of penetration cracks from TOFD images was also conducted using a backpropagation neural network [17]. By extracting multiscale features rather than single-scale features, classification accuracy can be increased.…”

Section: Automated Defect Detectionmentioning

confidence: 99%

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Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images

2022

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Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety.

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“…However, there are now new generation Corrosion Resistant Alloys (CRA) such as austenitic stainless steels in use. These materials have an anisotropic grain structure which makes conventional TOFD challenging due to scattering in the material [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Detecting contact acoustic nonlinearity in TOFD measurements via quasistatic loading

Carcione¹

2023

Materials Research Proceedings

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Abstract. In this paper, we propose a novel method to detect and quantify contact acoustic nonlinearity using conventional time of flight diffraction (TOFD) non-destructive testing (NDT) equipment, with the aim to improve the sensitivity and robustness of TOFD measurements. This new method involves applying an external cyclic quasi-static load while simultaneously taking TOFD measurements. The applied load causes modulation of contact surfaces within damaged areas of the material, which can be observed as changes in the time-domain TOFD response. Additional processing extracts any load-dependent features from the signals, allowing the identification and quantification of damage and defects that exhibit contact acoustic nonlinearity. Importantly, this new quasistatic contact acoustic nonlinearity (QS-CAN) technique maintains time-resolution and localisation capability of conventional TOFD. It is shown that the technique can differentiate between different types of damage such as fatigue cracks or voids within samples. The new QS-CAN nonlinear ultrasonic methodology is a fundamental extension to all existing nonlinear measurement techniques. It allows for the first time to use time signals captured from conventional NDT equipment to extract nonlinear material characteristics.

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Discrete Wavelet Transform based Denoising of TOFD Signals of Austenitic Stainless Steel Weld at Elevated Temperature

Cited by 8 publications

References 6 publications

Lateral wave removal of time-of-flight diffraction based on peak detection and correlation analysis

Lateral wave removal of time-of-flight diffraction based on peak detection and correlation analysis

Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images

Detecting contact acoustic nonlinearity in TOFD measurements via quasistatic loading

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