Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld

Abstract

As a technological advantage of ultrasonic non-destructive testing, fully focused imaging can accurately feedback the defective characteristics of the inspected object, greatly improving the detection efficiency. This article aims to address the challenges of outdated and low detection rates in the detection technology of branch pipe fillet welds. The full matrix acquisition (FMC) and total focus method (TFM) ultrasonic detection technology are used for detection and defect image feature analysis. Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. Through experiments, it has been found that this technology can effectively improve the detection efficiency of branch pipe weld defects, with a detection rate of 100%. Based on the partition fusion denoising method, the defect imaging quality can be further improved; at the same time, based on the feature extraction method, the error is 0.1 mm, the length range of various defects is 2.3 mm–6.3 mm, the width range is 0.6 mm–0.8 mm, and the angle range is 52°–75°, which can provide an application basis for the localization, classification, and risk assessment of corner weld defects in branch pipes.

Keywords

• FMC-TFM
• weld inspection
• noise reduction
• feature extraction