Physical perspective forward-inverse learning for ultrasonic sensing diagnosis in small diameter and thin-wall tube

Abstract

The ultrasonic testing method is a well-known non-destructive testing technique which has been applied to the tube inspection for guarantying the quality of the production. However, there exist several challenges to detect the defects of tubes with small diameter and thin-wall due to the complex of multiple reflections and waveform conversion. Parameters selection of the transducer takes key role to enhance the detection sensitivity such as frequency, size, refraction angle, distance offset, and focal point distance. This selection is generally dependent on human experience as it is highly time-consuming and subjective. In this paper, a novel parameter selection method based on physical perspective linked forward-inverse intelligence strategy has been proposed for ultrasonic immersed testing method. The optimized parameters can be calculated automatically while both testing and calibration repeated experiments can be avoided. The proposed method is computationally affordable and yields a high accuracy objective performance. Both simulation and experiments have been conducted to verify the efficacy of the proposed method.