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Quality Monitoring Research of Small Scale Resistance Spot Welding Based on Voltage Signal

机译：基于电压信号的小规模电阻点焊质量监测研究

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摘要

With the rapid development of microelectromechanical systems, small scale resistance spot welding (SSRSW) is ever-increasing used in electronic and medical devices. Whereas there is limited research work dealing with quality control of SSRSW. This paper investigated a real-time and in-situ SSRSW quality monitoring method by means of taking the voltage as the monitoring signature. It was obtained through clipping two leads onto the electrodes during SSRSW. As the linear DC and the high frequency (HF) resistance welding power supplies were the common equipments in SSRSW and constant current mode was used in this study, the variation of voltage with time indicated the conditions of the welding process which issued in the final weld quality. Utilizing four factors extracted from the voltage curve an artificial intelligence algorithm to estimate the weld quality was proposed. The maximum average forecast error of the trained network is about 0.15 mm, showing that the voltage curve is a reliable quality monitoring signature of SSRSW. The most prominent advantage of this method is that weld quality can be perfectly estimated with only two sensor clips compared with other methods reported for normal scale or large scale resistance spot welding (LSRSW).
机译：随着微机电系统的飞速发展，小型电阻点焊（SSRSW）越来越多地用于电子和医疗设备。而有关SSRSW质量控制的研究工作却很少。本文以电压为监测指标，研究了一种实时，现场的SSRSW质量监测方法。它是通过在SSRSW期间将两条引线夹在电极上而获得的。由于线性直流和高频（HF）电阻焊接电源是SSRSW的常用设备，并且在本研究中使用恒定电流模式，因此电压随时间的变化指示了最终焊接中发出的焊接过程的条件。质量。利用从电压曲线中提取的四个因素，提出了一种人工智能算法来估计焊接质量。训练网络的最大平均预测误差约为0.15毫米，这表明电压曲线是SSRSW的可靠质量监控信号。该方法最显着的优势是，与常规或大规模电阻点焊（LSRSW）报道的其他方法相比，仅用两个传感器夹就可以完美地评估焊接质量。

著录项

  • 来源
    《ISIJ international》 |2013年第2期|240-244|共5页
  • 作者

    Dawei ZHAO; Yuanxun WANG; Zongguo LIN; Suning SHENG;

  • 作者单位

    Department of Mechanics, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074 China;

    Department of Mechanics, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074 China;

    Grand Master Trading Limited, Miyachi Unitek Corporation, Nanjing, 211100 China;

    Grand Master Trading Limited, Miyachi Unitek Corporation, Nanjing, 211100 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    small scale resistance spot welding; voltage curve quality monitoring; artificial neural network;

    机译：小规模电阻点焊;电压曲线质量监控;人工神经网络;

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