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Online monitoring of resistance spot welding electrode wear state based on dynamic resistance

Author

Listed:
  • Lei Zhou

    (Harbin Institute of Technology)

  • Tianjian Li

    (Harbin Institute of Technology)

  • Wenjia Zheng

    (Harbin Institute of Technology)

  • Zhongdian Zhang

    (Harbin Institute of Technology)

  • Zhenglong Lei

    (Harbin Institute of Technology)

  • Laijun Wu

    (Harbin Institute of Technology at Weihai)

  • Shiliang Zhu

    (Xiamen Hongfa Hermetically Sealed Relays Co.)

  • Wenming Wang

    (Beijing Power Machinery Institute)

Abstract

The electrode state is an important factor affecting the welding quality in the process of resistance spot welding, but there is still lack of an effective method to monitor the electrode wear state. In this paper, a novel online monitoring method of electrode wear state is proposed by exploring the variation pattern of dynamic resistance. The evaluation method of time series similarity is important for dynamic resistance data processing, and two types of evaluation methods including static evaluation method and dynamic evaluation method are proposed in this paper. The static evaluation methods include shape change factor, dynamic resistance decrease ratio and peak time delay, and the dynamic evaluation method refers to the trend change factor. The welding process parameters and the welding material are kept unchanged during the experiment, the newly polished electrode is used to weld 1300 times continuously, and dynamic resistance is collected. According to the results of data processing, the change of the electrode state can be divided into three stages: The electrode state is stable when the welding number is less than 360. When the welding number is in the range of 360–800, the electrode state is in the transition stage, and the electrode state rapidly deteriorates with the increase of the welding number. When the welding number is more than 800, the electrode state is completely deteriorated, and the electrode needs to be dressed. This study may pave the way for online monitoring of electrode wear state.

Suggested Citation

  • Lei Zhou & Tianjian Li & Wenjia Zheng & Zhongdian Zhang & Zhenglong Lei & Laijun Wu & Shiliang Zhu & Wenming Wang, 2022. "Online monitoring of resistance spot welding electrode wear state based on dynamic resistance," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 91-101, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01650-6
    DOI: 10.1007/s10845-020-01650-6
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    References listed on IDEAS

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    1. Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
    2. Hamed Pashazadeh & Yousof Gheisari & Mohsen Hamedi, 2016. "Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 549-559, June.
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    Cited by:

    1. Sergey Butsykin & Anton Gordynets & Alexey Kiselev & Mikhail Slobodyan, 2023. "Evaluation of the reliability of resistance spot welding control via on-line monitoring of dynamic resistance," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3109-3129, October.

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