Analysis of influencing factors for ultrasonic flaw detection based on deep learning

Nondestructive evaluation of metal components using ultrasound is a widely adopted technique. Nevertheless, in practical applications, various factors may influence the outcomes of the testing. This study thoroughly examines the influencing factors of the ultrasonic testing process and employs deep learning techniques to explore how these factors affect the ultrasonic signals and the final testing results. Subsequently, a numerical simulation case was developed in COMSOL 6.2 to investigate how workpiece surface roughness, coupling materials, probe pressure affects the maximum amplitude of the ultrasonic signal, as well as their influence on the final testing results, thereby validating the effectiveness of our approach.