IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i3d10.1007_s10845-021-01844-6.html
   My bibliography  Save this article

Method for fusion of neighborhood rough set and XGBoost in welding process decision-making

Author

Listed:
  • Kainan Guan

    (Dalian Jiaotong University
    Dalian Jiaotong University)

  • Guang Yang

    (Dalian Jiaotong University
    Dalian Jiaotong University)

  • Liang Du

    (Dalian Jiaotong University
    Dalian Jiaotong University)

  • Zhengguang Li

    (Dalian Jiaotong University)

  • Xinhua Yang

    (Dalian Jiaotong University
    Dalian Jiaotong University)

Abstract

Correct decision-making rules are essential to achieve the application of knowledge. The welding procedure document requires a rigorous knowledge rule system. Due to the limitations in representing and extracting practical engineering knowledge, the construction of knowledge rules is complicated. This paper proposed a synergistic approach of fusion model and interpretation analysis. The fused model uses neighborhood rough sets and XGBoost to refine knowledge and constructs implicit relationships. Common logic rules and knowledge are replaced with the model. The model was validated and analyzed based on standardized high-speed train bogie framing engineering data, and the scores obtained were 0.89 for accuracy, 0.92 for Precision, 0.89 for Recall, and 0.89 for F1-score. Based on ensuring the metrics of the model, the interpretable analysis method expresses the implicit knowledge in the decision-making system. The tree model is used to explain the decision process, and the relationships of the attributes involved in the decision can be obtained via SHAP analysis. Moreover, it shows a high degree of consistency between interpretable results and actual engineering knowledge. The experimental results indicate that the proposed method can be effective for intelligent decision-making in welding procedure documentation.

Suggested Citation

  • Kainan Guan & Guang Yang & Liang Du & Zhengguang Li & Xinhua Yang, 2023. "Method for fusion of neighborhood rough set and XGBoost in welding process decision-making," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1229-1240, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01844-6
    DOI: 10.1007/s10845-021-01844-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01844-6
    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/s10845-021-01844-6?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. S.P. Leo Kumar, 2019. "Knowledge-based expert system in manufacturing planning: state-of-the-art review," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4766-4790, August.
    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. Shugui Wang & Yunxian Cui & Yuxin Song & Chenggang Ding & Wanyu Ding & Junwei Yin, 2024. "A novel surface temperature sensor and random forest-based welding quality prediction model," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3291-3314, October.
    2. John Taco & Pradeep Kundu & Jay Lee, 2024. "A novel technique for multiple failure modes classification based on deep forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3115-3129, October.

    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. Leal Filho, Walter & Wall, Tony & Rui Mucova, Serafino Afonso & Nagy, Gustavo J. & Balogun, Abdul-Lateef & Luetz, Johannes M. & Ng, Artie W. & Kovaleva, Marina & Safiul Azam, Fardous Mohammad & Alves,, 2022. "Deploying artificial intelligence for climate change adaptation," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    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:34:y:2023:i:3:d:10.1007_s10845-021-01844-6. 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.