Compensation method for metal defect detection based on deep learning and ultrasonic wave

Chunjie Wu; Zaisheng Ma; Yong Ding; Junlei Fu; Xinjia Wu

doi:10.1117/12.3038388

5 August 2024 Compensation method for metal defect detection based on deep learning and ultrasonic wave

Chunjie Wu, Zaisheng Ma, Yong Ding, Junlei Fu, Xinjia Wu

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 1322625 (2024) https://doi.org/10.1117/12.3038388
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

Ultrasonic nondestructive testing plays a crucial role in evaluating the integrity of metal structures. When there are defects in the tested workpiece, there will be reflection, refraction, diffraction, and other behaviors of sound waves. By analyzing the ultrasonic echo signal, the location and depth of defects can be identified. In previous ultrasonic defect detection, due to changes in the ultrasonic probe, the identified defects may change, affecting the accuracy of defect discrimination. This paper proposes a method for detecting the depth of metal cracks based on deep learning. This method combines finite element models and experimental analysis data to compensate for the depth of metal cracks, improving the accuracy of defect identification. Finally, an ultrasonic experimental study on metal components was conducted to verify the effectiveness of this method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chunjie Wu, Zaisheng Ma, Yong Ding, Junlei Fu, and Xinjia Wu "Compensation method for metal defect detection based on deep learning and ultrasonic wave", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 1322625 (5 August 2024); https://doi.org/10.1117/12.3038388

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