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Evaluation of the reliability of resistance spot welding control via on-line monitoring of dynamic resistance

Author

Listed:
  • Sergey Butsykin

    (‘Polyus’ Research and Production Center
    Tomsk Polytechnic University)

  • Anton Gordynets

    (Tomsk Polytechnic University)

  • Alexey Kiselev

    (Tomsk Polytechnic University)

  • Mikhail Slobodyan

    (Tomsk Scientific Center SB RAS)

Abstract

In resistance spot welding (RSW), initial resistance between electrodes (RBE) determines heat input (according to Joule’s law) and greatly affects the quality of joints. In turn, RBE values are characterized by substantial uncertainty and vary during the RSW processes. To reduce their dispersions, preliminary low-current pulses are applied. In some cases, the quality of the formed RSW joints are controlled using dynamic resistances obtained by feedback from advanced power sources. In these studies, the effect of four preheating current diagrams on the stabilization of the RBE values was investigated for a wide range of parts made of copper, brass, bronze, austenitic stainless steel, as well as aluminum, titanium and zirconium alloys with thicknesses from 0.2 to 1.0 mm in various combinations. Also, the RSW process control capabilities were assessed using feedback from an up-to-date digital synthesizer of unipolar current pulses. As a result, the RBE values were stabilized in all studied cases. Ranges of the variations between the maximum and minimum RBE values decreased from about 5–11 down to 2–5 times. However, the applied algorithms of the preheating current pulses had no effect on the RBE dispersions. It was found that dynamic electrical processes in a welding gun cause distortion of actual RBE curves, which makes it difficult to control heat input and, respectively, the formation of weld nuggets.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01987-0
    DOI: 10.1007/s10845-022-01987-0
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    References listed on IDEAS

    as
    1. Baifan Zhou & Tim Pychynski & Markus Reischl & Evgeny Kharlamov & Ralf Mikut, 2022. "Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1139-1163, April.
    2. Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Dongjie Liang & Wenhao Du, 2021. "Multi-objective optimization of the resistance spot welding process using a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2219-2234, December.
    3. 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.
    4. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    5. 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.
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