IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i7d10.1007_s10845-020-01627-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01627-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-020-01627-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Dongbo Wu & Hui Wang & Kaiyao Zhang & Bing Zhao & Xiaojun Lin, 2020. "Research on adaptive CNC machining arithmetic and process for near-net-shaped jet engine blade," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 717-744, March.
    3. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    4. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    5. Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.
    6. Liang Tian & Yu Luo, 2020. "A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 575-596, March.
    7. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    8. 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.
    9. 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.
    10. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01627-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.