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Multi-objective optimization of the resistance spot welding process using a hybrid approach

Author

Listed:
  • Dawei Zhao

    (South Ural State University
    Xi’an Jiaotong University)

  • Mikhail Ivanov

    (South Ural State University)

  • Yuanxun Wang

    (Huazhong University of Science and Technology)

  • Dongjie Liang

    (Guangxi Zhuang Autonomous Region Institute of Metrology and Test)

  • Wenhao Du

    (Hunan Institute of Engineering)

Abstract

This study proposed an approach to optimize the process parameters using the entropy weight method combining regression analysis in the resistance spot welding process. Based on the central composite experimental design, tests were carried out with three levels of process parameters for spot-welded titanium alloy sheets. Multiple quality characteristics, namely nugget diameter, maximum displacement, tensile shear load, and failure energy, were converted into a comprehensive welding quality index. The weight for each quality index to obtain the comprehensive welding quality index was determined based on the grey entropy method. The welding heat input for each welding joints was calculated based on the dynamic power signal in the welding process. The mathematical model correlating process parameters and the comprehensive welding quality index was established on the basis of regression analysis. The relationship between the welding process parameters and welding heat was also quantified using a regression model. The effects of welding process parameters on welding quality and welding heat were also discussed. To optimize multi-performance characteristics, the desirability function was employed. The verification test results proved that the method proposed in this paper effectively optimized the welding parameters and kept the welding heat input as low as possible at the same time. Welding current is the most significant parameter affecting the welding quality followed by welding time. This can be owing to its direct influence on the amount of heat supplied to the welding zone during the welding process. The method proposed in this study can serve as a guidance and recommendation for resistance spot welding welders to guarantee welding quality and meet the needs of high production and effective energy saving.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:8:d:10.1007_s10845-020-01638-2
    DOI: 10.1007/s10845-020-01638-2
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    References listed on IDEAS

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    1. 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.
    2. Pauline Ong & Choon Sin Ho & Desmond Daniel Vui Sheng Chin & Chee Kiong Sia & Chuan Huat Ng & Md Saidin Wahab & Abduladim Salem Bala, 2019. "Diameter prediction and optimization of hot extrusion-synthesized polypropylene filament using statistical and soft computing techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1957-1972, April.
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    Cited by:

    1. Jiyoung Jung & Kundo Park & Byungjin Cho & Jinkyoo Park & Seunghwa Ryu, 2023. "Optimization of injection molding process using multi-objective bayesian optimization and constrained generative inverse design networks," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3623-3636, December.
    2. 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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