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Welding quality evaluation of resistance spot welding based on 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)

  • Wenhao Du

    (Hunan Institute of Engineering)

Abstract

In this investigation, the welding quality of TC2 titanium alloy with 0.4 mm thickness was predicted using two regression models and an artificial neural network model. The welding current and the voltage between the upper and lower electrodes were obtained using the Rogowski coil and a line voltage sensor. And then the variations of the dynamic resistance curve and the effects of the welding current and welding time on the dynamic resistance signals were investigated. The principal component analysis (PCA) was employed to eliminate the redundant information in the dynamic resistance curve and characterize the shape information of the entire dynamic resistance. A linear regression model quantifying the relationship between the nugget diameter and the principal components was established. The results of the analysis of variance indicated that the performance of this regression equation was very good. Some statistical characteristics of the dynamic resistance signal were also extracted to investigate the relationship between the nugget diameter and dynamic resistance. The results indicated that the regression model established based on the PCA technique was much more robust than the model developed on the basis of the features manually extracted from the dynamic resistance signal. The neural network model was also used to predict the nugget diameter of the welding joints utilizing the extracted features. The performances of the three established prediction models were compared and their behavioral discrepancies were also investigated. The PCA technique not only can minimize the prior assumptions about the certain shape of the dynamic resistance curve and remove the subjective factors caused by the manual extraction method, but it also can assess and monitor the welding quality with a good level of reliability.

Suggested Citation

  • Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Wenhao Du, 2021. "Welding quality evaluation of resistance spot welding based on a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1819-1832, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01627-5
    DOI: 10.1007/s10845-020-01627-5
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    References listed on IDEAS

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    1. Shilpi Kumari & Rahul Jain & Ujjwal Kumar & Inderjeet Yadav & Nitin Ranjan & Kanchan Kumari & Ram Kumar Kesharwani & Sachin Kumar & Srikanta Pal & Surjya K. Pal & Debashish Chakravarty, 2019. "Defect identification in friction stir welding using continuous wavelet transform," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 483-494, February.
    2. Kamran Javed & Rafael Gouriveau & Xiang Li & Noureddine Zerhouni, 2018. "Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1873-1890, 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. Neeraj Sharma & Kamal Kumar & Tilak Raj & Vinod Kumar, 2019. "Porosity exploration of SMA by Taguchi, regression analysis and genetic programming," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 139-146, January.
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