Zhang Jing, Zhang Guocai, Chen Zijie, Zou Hailin, Xue Shuai, Deng Jianjie, Li Jianqing
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China.
School of Applied Science and Civil Engineering, Beijing Institute of Technology, Zhuhai 519006, China.
Sensors (Basel). 2024 Jun 28;24(13):4199. doi: 10.3390/s24134199.
The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.
识别焊缝中的夹渣缺陷对于保证焊接结构的完整性、安全性和延长使用寿命至关重要。大多数研究集中在不同类型的焊接缺陷上,但关于夹渣材料类别的分支研究有限,而这对于保障工程质量和人员安全至关重要。为了解决这个问题,我们设计了一种使用超声波检测来识别奥氏体不锈钢中夹杂物材料类别的模拟方法。它基于在水环境中的模拟实验,选择了包括四种金属材料和两种非金属材料在内的六类立方试件来模拟夹渣缺陷的渣材料。采用粒子群优化算法优化的变分模态分解对超声信号进行去噪。此外,利用去噪信号的相位谱提取水 - 渣试件界面回波信号的相位特征。实验结果表明,我们的方法具有分解适度、去噪性能良好的特点。与著名的信号去噪算法相比,在焊缝夹渣缺陷信号去噪中,所提方法从回波信号中提取的本征模态函数数量最少,信噪比最高,归一化互相关最低。最后,相位谱可以根据回波信号相位中是否存在半波损失来确定夹渣相对于焊缝母材是较厚还是较薄的介质。