Quality Enhancement of Ultrasonic TOFD Signals from Carbon Steel Weld Pad with Notches
Quality Enhancement of Ultrasonic TOFD Signals from Carbon Steel Weld Pad with Notches
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DOI：
10.1016/j.ultras.2017.11.001
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发表时间：
2018-03-01
期刊：
影响因子：
4.2
通讯作者：
Venkatraman, B.
中科院分区：
文献类型：
--
作者：
Manjula, K.;Vijayarekha, K.;Venkatraman, B.
焊接是工业中部件制造的一个组成部分。尽管焊接的科学和艺术已有100多年的历史，但焊接过程中仍会出现缺陷。操作规程要求对焊缝进行测试和评估。传统的超声波检测已广泛应用于工业中，用于检测和评估焊接件中的缺陷/缺陷。随着传感器和信号分析技术的进步，在过去的二十年里，超声检测领域得到了广泛的发展。我们拥有先进的技术，如飞行时间衍射（TOFD），它具有更好的线性缺陷检测概率。TOFD应用过程中的一个主要刺激因素，特别是在碳钢焊接件的测试中，是噪音的存在。国际上已采用多种方法来抑制这种噪音，每种方法都有其优点和缺点。研究了一种利用离散小波变换抑制噪声的方法来增强碳钢焊件TOFD A扫描信号。分析清楚地表明，小波变换给出了更好的信噪比改善使用高阶小波滤波器与4级小波分解。然而，这种信号增强的计算成本取决于沿着选择的小波滤波器以及选择的DWT分解级别。(C)2017爱思唯尔B. V.保留所有权利。
Welding is an integral part of component fabrication in industry. Even though the science and art of welding are more than 100 years old, defects continue to occur during welding. Codes of practice require that the welds be tested and evaluated. Conventionally ultrasonic testing has been widely applied in industry for the detection and evaluation of the flaws/defects in the weldments. With advances in sensor and signal analysis technologies, the last two decades have seen extensive developments in the field of ultrasonic testing. We have advanced techniques such as Time of Flight Diffraction (TOFD) which has better probability of detection for linear defects. A major irritant during the application of TOFD, especially for the testing of carbon steel weldments, is the presence of noise. A variety of approaches has been used internationally for the suppression of such noise and each has its own merits and demerits. This paper focuses on a method of enhancing the TOFD A-scan signals in carbon steel weldments by suppressing the noise from them using the discrete wavelet transform (DWT). The analysis clearly indicates that the DWT gives better signal-to-noise ratio improvement using higher-order wavelet filters with 4-level DWT decomposition. However the computational cost of this signal enhancement depends on the wavelet filter chosen along with the chosen level of DWT decomposition. (C) 2017 Elsevier B.V. All rights reserved.