| 薄板点焊超声检测信号特征分析与缺陷识别 |
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| 引用本文： | 吴刚,关山月,汪小凯,王彬.薄板点焊超声检测信号特征分析与缺陷识别[J].焊接学报,2019,40(4):112-118. |
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| 作者姓名： | 吴刚 关山月 汪小凯 王彬 |
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| 作者单位： | 三峡大学 机械与动力学院,宜昌,443002;武汉理工大学 现代汽车零部件技术湖北省重点实验室,武汉 430070;武汉理工大学 汽车零部件技术湖北省协同创新中心,武汉 430070 |
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| 摘 要： | 运用低碳钢薄板点焊超声检测有限元仿真模型，对气孔、压痕过深、熔核过小、脱焊等四种缺陷类型的点焊检测超声仿真信号进行快速傅里叶变换得到其频谱图，并采用统计学方法分别提取了超声信号时域和频域特征值.通过分析超声在不同缺陷焊点内部的传播规律，以及特征值的变化规律，总结了点焊缺陷类型的识别方法.利用该方法对大量点焊试样超声检测试验信号进行缺陷识别并与金相试验结果对比分析. 结果表明，综合分析超声检测信号时域和频域特征值规律，能够有效地识别点焊缺陷类型.
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| 关 键 词： | 薄板点焊 超声检测 特征值提取 缺陷识别 |
| 收稿时间： | 2017/8/9 0:00:00 |
Feature analysis and defect recognition of ultrasonic detection signal for spot welding of sheet |
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| WU Gang,GUAN Shanyue,WANG Xiaokai,WANG Bin.Feature analysis and defect recognition of ultrasonic detection signal for spot welding of sheet[J].Transactions of The China Welding Institution,2019,40(4):112-118. |
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| Authors: | WU Gang GUAN Shanyue WANG Xiaokai WANG Bin |
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| Affiliation: | 1. College of Mechanical and Power Engineering, China Three Gorges University, Yichang 443002, China;2. Hubei Key Laboratory of Modern Automotive Components and Technology, Wuhan University of Technology, Wuhan 430070, China;3. Hubei Collaborative Innovation Center for Automotive Components and Technology, Wuhan 430070, China |
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| Abstract: | Based on the finite element simulation models of spot welding ultrasonic detection for low carbon steel and Fast Fourier Transform, the frequency spectrums of four kinds of simulated signals, such as pore, deep indentation, small nugget, loose weld, were obtained. And the time domain and frequency domain features of signals were extracted respectively by statistical methods. By analyzing the propagation rules of ultrasonic in different defect spot welds and the change rules of features, the recognition methods of different defects types of spot welding were summarized. The method was used to identify the defects of a large number of spot welding samples by ultrasonic testing signals, and the results were compared with the metallographic test results. The results showed that the time domain and frequency domain features of ultrasonic detection signals were effectively recognized the types of spot welding defects. |
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| Keywords: | sheet spot welding|ultrasonic testing|features extraction|defects recognition |
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