IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i3d10.1007_s10845-019-01469-w.html
   My bibliography  Save this article

A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm

Author

Listed:
  • Liang Tian

    (Shijiazhuang Tiedao University
    Shanghai Jiaotong University
    CRCC Bridge Engineering Bureau Group Co., Ltd)

  • Yu Luo

    (Shanghai Jiaotong University)

Abstract

The inherent deformation method has a significant advantage in evaluating the total welding deformations for large and complex welded structures. The prerequisite for applying this approach is that the inherent deformations of corresponding weld joints should be known beforehand. In this study, an intelligent model based on support vector machine (SVM) and genetic algorithm (GA) was established to predict the inherent deformations of a fillet-welded joint. The training samples were obtained from numerical experiments conducted by the thermal–elastic–plastic finite element analysis. In the developed SVM model, the welding speed, current, voltage and plate thickness were considered as input parameters, and the longitudinal and transverse inherent deformations were corresponding outputs. The correlation coefficients and percentage errors for all the samples were calculated to evaluate the prediction performance of the SVM model. The research results demonstrate that the SVM model optimized by GA can be used to assess the longitudinal and transverse inherent deformations for the T-joint fillet weld with acceptable accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01469-w
    DOI: 10.1007/s10845-019-01469-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-019-01469-w
    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/s10845-019-01469-w?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. Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
    2. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    3. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    4. Roham Sadeghi Tabar & Kristina Wärmefjord & Rikard Söderberg & Lars Lindkvist, 2021. "Critical joint identification for efficient sequencing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 769-780, March.

    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. 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.
    3. Christian Kubik & Sebastian Michael Knauer & Peter Groche, 2022. "Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 259-282, 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. 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.
    6. 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.
    7. Guiqian Liu & Xiangdong Gao & Deyong You & Nanfeng Zhang, 2019. "Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 821-832, February.
    8. 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.
    9. 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:31:y:2020:i:3:d:10.1007_s10845-019-01469-w. 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.