Research on Rapid Photoacoustic Imaging Method of Corrugated Composite Plate Interface Based on Adaptive Compressive Sensing

doi:10.3901/JME.260106

Abstract

Abstract: The efficient detection of complex interface defects in corrugated rolling processes is a key challenge for improving the quality of metal clad plates. Traditional ultrasonic imaging methods suffer from location deviation and insufficient efficiency when detecting corrugated interfaces. To address these issues, a new photoacoustic imaging method that integrates adaptive compressed sensing and an improved phase-shift migration method is proposed. By constructing a piecewise constant velocity function model and using a windowed calculation method, the imaging distortion problem at the bottom surface of the second medium, which is common in traditional phase-shift migration methods, is effectively resolved. An innovative adaptive sampling strategy based on signal permutation entropy is introduced to optimize data acquisition while maintaining imaging quality using compressed sensing technology.The experimental results show that compared with the traditional method, this method can correct the position and shape deviations in the imaging of the bottom surface of the second medium by the traditional method. After using the adaptive compressive sensing method with a segment number of 6 and a sampling rate of 0.3, the calculation time is reduced by 82%, the standard deviation of the reconstructed signal is reduced by 82%, and the PSNR value of the imaging result is increased by 5.7%. This method is superior to the traditional algorithm in both imaging accuracy and speed, providing a new idea for the rapid imaging detection of metal composite plates with complex interfaces.

Key words: corrugated composite plate, laser ultrasonic, interface imaging, compressed sensing

CLC Number:

TG156

ZHANG Yanjie, XU Zhihui, LI Yu, WANG Tao, YANG Quan, JIANG Ruipeng, WANG Wei. Research on Rapid Photoacoustic Imaging Method of Corrugated Composite Plate Interface Based on Adaptive Compressive Sensing[J]. Journal of Mechanical Engineering, 2026, 62(4): 61-74.

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