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Multi-defect detection based on ultrasonic Lamb wave sign phase coherence factor imaging method.

Authors :
Meng-Ke Zhang
Guo-Peng Fan
Wen-Fa Zhu
Shu-Bin Zheng
Xiao-Dong Chai
Wei-Lv
Hui-Zhang
Source :
Insight: Non-Destructive Testing & Condition Monitoring. Nov2021, Vol. 63 Issue 11, p659-666. 8p.
Publication Year :
2021

Abstract

The ultrasonic Lamb wave total focusing method (TFM) only uses the amplitude of the defective scattered signal for virtual focused imaging, while ignoring the phase information of the scattered signal and the dispersion characteristics of the Lamb wave, resulting in low imaging resolution and easily produced artefacts in imaging. To solve this problem, an ultrasonic Lamb wave imaging method based on phase coherence is proposed in this paper and the sign coherence factor (SCF) in the full matrix scattering signal is extracted. Moreover, the method uses the SCF to weight the amplitude of the full matrix scattering signal, suppresses the side lobes of the defect echo signal and the Lamb wave dispersion effect, improves the ultrasonic Lamb wave imaging resolution and weakens the artefacts. Finally, single- and multiplehole defects in aluminium plates are detected for experimental validation using an ultrasonic phased array. The array performance indicator and signal-to-noise ratio are used as indicators for quantitative assessment of imaging performance. The results show that compared with the TFM imaging, the SCF imaging can effectively suppress the noise and scattered signal side lobes, improve the array performance indicator (API) by 69.1% and improve the signal-to-noise ratio (SNR) by 73.9%. In addition, the SCF imaging can effectively weaken the interference of scattered signals between multiple through-hole defects, resulting in fewer artefacts in imaging. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13542575
Volume :
63
Issue :
11
Database :
Academic Search Index
Journal :
Insight: Non-Destructive Testing & Condition Monitoring
Publication Type :
Academic Journal
Accession number :
153671480
Full Text :
https://doi.org/10.1784/insi.2021.63.11.659