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通过创新的弯底探头设计增强飞行时间衍射(TOFD)检测

Enhancing Time-of-Flight Diffraction (TOFD) Inspection through an Innovative Curved-Sole Probe Design.

作者信息

Sanchez Duo Irati, Lanzagorta Jose Luis, Aizpurua Maestre Iratxe, Galdos Lander

机构信息

IDEKO Member of Basque Research and Technology Alliance, 20870 Elgoibar, Gipuzkoa, Spain.

Faculty of Engineering, Mondragon Unibertsitatea, 20500 Mondragon, Gipuzkoa, Spain.

出版信息

Sensors (Basel). 2024 Sep 30;24(19):6360. doi: 10.3390/s24196360.

DOI:10.3390/s24196360
PMID:39409400
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11479281/
Abstract

Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach.

摘要

飞行时间衍射(TOFD)是一种超声检测(UT)方法,作为一种主要用于焊缝检测的无损检测(NDT)技术已被广泛应用。与其他成熟的超声检测技术相比,TOFD能够识别不连续缺陷,而不论其方向如何。本文提出了一种典型TOFD换能器的重新设计,其特点是采用了创新的弯曲底面,旨在增强其缺陷检测能力。这种设计对厚壁样品特别有益,因为它可以在不影响表面区域附近分辨率的情况下进行更深层次的检测。在这项研究中,进行了一项评估,包括模拟超声束分布以及对具有不同深度人工制造缺陷的部件进行实验测试,以验证新设计。结果表明,与传统方法相比,波束分布面积提高了30%至50%,信噪比(SNR)也有所改善,缺陷检测能力提高了24%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/a8f229e80a56/sensors-24-06360-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/f06d97c28417/sensors-24-06360-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/60ded9e06388/sensors-24-06360-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/d3f110ea593b/sensors-24-06360-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/d62f154812b9/sensors-24-06360-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/a8f229e80a56/sensors-24-06360-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/f06d97c28417/sensors-24-06360-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/60ded9e06388/sensors-24-06360-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/d3f110ea593b/sensors-24-06360-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/d62f154812b9/sensors-24-06360-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b863/11479281/a8f229e80a56/sensors-24-06360-g007.jpg

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本文引用的文献

1
A review of synthetic and augmented training data for machine learning in ultrasonic non-destructive evaluation.超声无损检测中用于机器学习的合成与增强训练数据综述。
Ultrasonics. 2023 Sep;134:107041. doi: 10.1016/j.ultras.2023.107041. Epub 2023 May 18.
2
Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review.用于焊接缺陷超声无损检测的机器学习:一项系统综述。
Ultrasonics. 2023 Jan;127:106854. doi: 10.1016/j.ultras.2022.106854. Epub 2022 Sep 26.