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Machine Learning-Based Predictive Modelling of Spot-Welding Process Parameters

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Intelligent Computing and Optimization (ICO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 729))

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Abstract

This research paper presents a comparative study of machine learning (ML) algorithms for the predictive modelling of spot-welding process parameters. The focus is on predicting the nugget diameter (ND) and nugget height (NH) using four different ML algorithms (namely, linear regression, random forest regression, adaptive boosting regression, and support vector machine regression). An experimental dataset consisting of fifty data points is used in this study. Exploratory data analysis was carried out to investigate the effect of process parameters on the response variables. Performance evaluation metrics were employed to gauge the predictive accuracy of the ML models. The results indicate that the random forest regression demonstrates superior performance in predicting both ND and NH compared to the other algorithms. This study can serve as a foundation for further research in optimizing the spot-welding process through machine learning techniques.

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References

  1. Rout, A., Deepak, B.B.V.L., Biswal, B.B.: Advances in weld seam tracking techniques for robotic welding: a review. Robotics and Computer-Integrated Manufacturing 56, 12–37 (2019)

    Article  Google Scholar 

  2. Zhao, D., Ivanov, M., Wang, Y., Du, W.: Welding quality evaluation of resistance spot welding based on a hybrid approach. J. Intell. Manuf. 32, 1819–1832 (2021)

    Article  Google Scholar 

  3. Arifin, A.: Dissimilar metal welding using shielded metal arc welding: a review. Technology Reports of Kansai University 62 (2020)

    Google Scholar 

  4. Tanaka, K., Shigeta, M., Komen, H., Tanaka, M.: Electrode contamination caused by metal vapour transport during tungsten inert gas welding. Sci. Technol. Weld. Join. 26, 258–263 (2021)

    Article  Google Scholar 

  5. Rai, R., De, A., Bhadeshia, H.K.D.H., DebRoy, T.: Friction stir welding tools. Sci. Technol. Weld. Joining 16, 325–342 (2011)

    Article  Google Scholar 

  6. Kahraman, N.: The influence of welding parameters on the joint strength of resistance spot-welded titanium sheets. Mater. Des. 28, 420–427 (2007)

    Article  Google Scholar 

  7. Sudhagar, S., Sakthivel, M., Ganeshkumar, P.: Monitoring of friction stir welding based on vision system coupled with machine learning algorithm. Measurement 144, 135–143 (2019)

    Article  Google Scholar 

  8. Kumar, B.P., Vijayakumar, Y.: Optimization of shielded metal arc welding parameters for welding of pipes by using Taguchi approach. Int. J. Eng. Sci. Technol. 4, 2083–2088 (2012)

    Google Scholar 

  9. Mishra, A., Sefene, E.M., Tsegaw, A.A.: Process Parameter Optimization of Friction Stir Welding on 6061AA Using Supervised Machine Learning Regression-based Algorithms. arXiv preprint arXiv:2109.00570 (2021)

  10. Liang, R., Yu, R., Luo, Y., Zhang, Y.: Machine learning of weld joint penetration from weld pool surface using support vector regression. J. Manuf. Process. 41, 23–28 (2019)

    Article  Google Scholar 

  11. Satpathy, M.P., Mishra, S.B., Sahoo, S.K.: Ultrasonic spot welding of aluminum-copper dissimilar metals: a study on joint strength by experimentation and machine learning techniques. J. Manuf. Process. 33, 96–110 (2018)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Dinesh V. Burande & Kanak Kalita

  2. Department of Mechanical Engineering, University Centre for Research and Development, Chandigarh University, Mohali, India

    Jasgurpeet Singh Chohan

Authors
  1. Dinesh V. Burande
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  2. Kanak Kalita
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  3. Jasgurpeet Singh Chohan
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Corresponding author

Correspondence to Kanak Kalita .

Editor information

Editors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Ho Chi Minh City, Vietnam

    Pandian Vasant

  2. Department of Computer Science, Chittagong University of Engineering & Technology, Chittagong, Bangladesh

    Mohammad Shamsul Arefin

  3. Federal Scientific Agroengineering Center VIM, Laboratory of Non-traditional Energy Systems, Russian University of Transport, Department of Theoretical and Applied Mechanics, 127994 Moscow, Russia;, Moscow, Russia

    Vladimir Panchenko

  4. Department of Computer Science, UOW Malaysia KDU Penang University Colleage, George Town, Malaysia

    J. Joshua Thomas

  5. Northwest University, Mmabatho, South Africa

    Elias Munapo

  6. Faculty of Engineering Management, Poznań University of Technology, Poznan, Poland

    Gerhard-Wilhelm Weber

  7. Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Mexico City, Mexico

    Roman Rodriguez-Aguilar

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Burande, D.V., Kalita, K., Chohan, J.S. (2023). Machine Learning-Based Predictive Modelling of Spot-Welding Process Parameters. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-031-36246-0_32

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