Abstract—Ultrasonic nondestructive testing has been widely used in the detection and evaluation of fatigue cracks and defects of high-speed railway. In order to improve the detection speed of cylindrical components such as rims, a theoretical model of the forward vector algorithm of ultrasonic immersion curved surface is established. For improving image quality, an averaging correction factor is introduced in this paper to avoid multiple irradiation of the same pixel by the same element, and the visualization of cylindrical components with internal defects is realized by simulation and experiment. The traditional synthetic aperture focusing technique (SAFT), Newton iterative synthetic aperture (NISA) and ultrasonic forward vector algorithm (UFVA) are compared and analyzed on the aspects of imaging effect, array performance index (API) and imaging efficiency. Compared with the other two algorithms, the ultrasonic forward vector algorithm can effectively overcome the iteration of calculation of the refraction point and improve the imaging efficiency. The efficiency is improved by 2 and 1 orders of magnitude respectively while imaging quality is guaranteed. At the same time, this algorithm plays an important role on the measurement of the inner and outer diameter of the rim, the defect detection, and the quantitative analysis of the surface wear, which provides an effective means for the rapid detection of internal defects of the regular curved parts.
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ACKNOWLEDGMENTS
The work was funded by National Nature Science Foundation of China (Grant no. 61471304) and Sichuan Science and Technology Plan Project Foundation of China (2015GZ0302) and the authors wish to acknowledge them for their support. The authors also thank Southwest Jiaotong University NDT Research Center and Olympus NDT Joint Laboratory of Non-destructive Testing for their kind support in the experiments.
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Yongjun Li, Wang, Z., Zhang, Y. et al. Synthetic Aperture Imaging in Cylindrical Component Using Ultrasonic Immersion Forward Vector Algorithm. Russ J Nondestruct Test 56, 397–407 (2020). https://doi.org/10.1134/S106183092005006X
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DOI: https://doi.org/10.1134/S106183092005006X