An optimized ultrasonic C-scan imaging method for diffusion bonding based on echo denoising

LI Yu, JIANG Yinying, CHANG Qing

doi:10.3969/j.issn.1007-130X.2026.05.010

Computer Engineering & Science >

2026 , Vol. 48 >Issue 5: 865 - 875

DOI: https://doi.org/10.3969/j.issn.1007-130X.2026.05.010

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An optimized ultrasonic C-scan imaging method for diffusion bonding based on echo denoising

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(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

Received date: 2024-05-09

Revised date: 2024-11-03

Online published: 2026-05-21

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Abstract

Aiming at the challenging issue of titanium alloy diffusion bonding interface defect detection in the field of nondestructive testing, an optimized ultrasonic C-scan imaging method for diffusion bonding based on echo denoising is proposed to enhance the detection capability for minute defects. This method achieves noise reduction by utilizing ensemble empirical mode decomposition (EEMD) combined with wavelet soft-threshold denoising to reconstruct ultrasonic echo signals. The imaging method is optimized according to the characteristics of interface waves from minute defects, employing the peak-to-valley difference as a new feature value instead of the gate amplitude for C-scan imaging to highlight minute defects. Additionally, image enhancement and denoising techniques are integrated to further optimize imaging quality and improve defect detection capability. Practical testing on artificial defect samples demonstrates that, compared to existing conventional ultrasonic C-scan imaging and other comparative imaging methods, the proposed method exhibits smaller errors between the detected defect lengths and the actual metallographic dimensions, effectively detecting minute defects within the specimens.

Key words： ultrasonic detection; diffusion bonding; minute defect; imaging optimization; feature value

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LI Yu, JIANG Yinying, CHANG Qing . An optimized ultrasonic C-scan imaging method for diffusion bonding based on echo denoising[J]. Computer Engineering & Science, 2026 , 48(5) : 865 -875 . DOI: 10.3969/j.issn.1007-130X.2026.05.010

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