| Citation: | ZHU Tiantian, SONG Bo, MAO Jie, et al. PAUT data intelligent analysis method of welding seams based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(3): 504-513. doi: 10.13700/j.bh.1001-5965.2020.0578(in Chinese)
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In the welding seam phased array ultrasonic testing (PAUT), the traditional manual judgment method is used to identify and locate the defects in the inspection data. However, this method has lower interpretation efficiency and higher requirements for the experience of the inspectors, and it has difficulty for meeting the requirements of automated ultrasound inspection. In this paper, combined with the features of S and B scan images of welding seam PAUT and 3D structure of weld, an intelligent recognition model based on target detection and tracking algorithm in deep learning is proposed to identify and locate the weld defect automatically. The experimental results show that the average value of 3D IOU of the defects (the average intersection ratio of the predicted and the actual 3D defect frame) reaches 0.644 9, which is close to the real defects' location. This method can realize the intelligent recognition and positioning from PAUT imaging data in welding seam.
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