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Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

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Abstract

Resistance spot welding (RSW) is a highly used joining procedure in automotive industry. In RSW, after a number of welds the welding electrode starts to wear and its diameter changes. This causes the weld nugget diameter abnormal variations and consequently reduces the weld strength. Therefore the tip of the electrode should be dressed in RSW. Selecting the optimum time for the welding electrode tip dressing operations is very important. In this research three welding parameters including the welding time, the welding current, and the welding pressure were identified as the main effective parameters on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. Then using hybrid combination of the artificial neural networks and multi-objective genetic algorithm, the optimized values of the aforementioned parameters were specified. Finally experiments were fulfilled to estimate the admissible number of the weld spots which should be done before the electrode tip dressing operation.

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Notes

  1. KES C-G006 P.14 STANDARD.

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Authors and Affiliations

  1. Young Researchers and Elites Club, East Azarbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran

    Hamed Pashazadeh

  2. Bushehr Branch, Department of Mathematics, Islamic Azad University, Bushehr, Iran

    Yousof Gheisari

  3. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohsen Hamedi

Authors
  1. Hamed Pashazadeh
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  2. Yousof Gheisari
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  3. Mohsen Hamedi
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Correspondence to Hamed Pashazadeh.

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Pashazadeh, H., Gheisari, Y. & Hamedi, M. Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm. J Intell Manuf 27, 549–559 (2016). https://doi.org/10.1007/s10845-014-0891-x

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  • DOI: https://doi.org/10.1007/s10845-014-0891-x

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