Evaluation of Quality Diffusion Bonding in Similar Material (Cu/Cu) Using Ultrasonic ‘C’ Scan Testing Method

Kumar, S. Suresh; Ravisankar, B.

doi:10.4028/www.scientific.net/amm.592-594.289

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The graph represents strength ratio decreasing with the reflection co efficient increase or vice versa and hence proving the quality of the bond. The bond quality is determined by the strength ratio and two NDT parameters [23,24]. The results correlate well.…”

Section: Strength Ratiomentioning

confidence: 96%

Destructive and non-destructive evaluation of copper diffusion bonds

Kumar

Ravisankar

2016

Journal of Manufacturing Processes

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Section: Strength Ratiomentioning

confidence: 96%

Destructive and non-destructive evaluation of copper diffusion bonds

Kumar

Ravisankar

2016

Journal of Manufacturing Processes

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“…At this point, φ(t) satisfies the constraint. Define the wavelet transform expressions of φ(t) and φ 2 (t), as shown in Equations ( 5) and (6).…”

Section: Theoretical Backgrounds 21 Wavelet Transformmentioning

confidence: 99%

“…Piezoelectric ultrasonic inspection [4] is distinguished by its high resolution, simple operation, and resistance to external environmental interference; its application in water immersion ultrasonic pulse echo technology has wide application prospects in coating immersion ultrasonic pulse echo technology has wide application prospects in coating inspection [5]. For the conventional ultrasonic signature scanning technique [6], the vertical incidence of ultrasound reduces the effect of waveform conversion on the echo signal, and the received reflected echo can be directly analyzed to obtain the necessary time-domain information. In the process of assembled steel structure coating inspection, the coating thickness is extremely thin, resulting in the mixing of the reflected echoes from the interface of fireproof coating and anticorrosive coating, which makes it difficult to identify the characteristic information of each coating and impossible to precisely determine the coating thickness, as depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Detection of Aliased Signal Separation Based on Adaptive Feature Dictionary and K–SVD Algorithm for Protective Coatings of Assembled Steel Structure

et al. 2023

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When using ultrasound to detect the thickness of protective coatings on assembled steel structures, the coatings are extremely thin, which can cause echo signals to overlap and impair the detection accuracy. Therefore, the study of the separation of the superimposed signals is essential for the precise measurement of the thickness of thinner coatings. A method for signal time domain feature extraction based on an adaptive feature dictionary and K–SVD is investigated. First, the wavelet transform, which is sensitive to singular signal values, is used to identify the extreme values of the signal and use them as the new signal to be processed. Then, the feature signal extracted by wavelet transform is transformed into Hankel matrix form, and the initial feature dictionary is constructed by period segmentation and random extraction. The optimized feature dictionary is subsequently obtained by enhancing the K–SVD algorithm. Finally, the time domain signal is reconstructed using the optimized feature dictionary. Simulations and experiments demonstrate that the method is more accurate in separating mixed signals and extracting signal time domain feature information than the conventional wavelet transform and Gabor dictionary-based MP algorithm, and that it is more advantageous in detecting the thickness of protective coatings.

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