IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i2d10.1007_s10845-021-01827-7.html
   My bibliography  Save this article

Part machining feature recognition based on a deep learning method

Author

Listed:
  • Fangwei Ning

    (Beihang University)

  • Yan Shi

    (Beihang University)

  • Maolin Cai

    (Beihang University)

  • Weiqing Xu

    (Beihang University)

Abstract

Machining feature recognition is a key step in computer-aided process planning to improve the level of design and manufacturing, production efficiency, and competitiveness. Although the traditional feature recognition method using a graph-based approach has advantages in feature logic expression, the calculation process is inefficient. Deep learning is a new technology that can automatically learn complex mapping relationships and high-level data features from a large amount of data. Therefore, this classification technology has been successfully and widely used in various fields. This study examined a three-dimensional convolutional neural network combined with a graph-based approach, taking advantage of deep learning technology and traditional feature recognition methods. First, the convex and concave machining features of a part were determined using an attributed adjacency graph. Then, the machining features were separated using the bounding box method and voxelized. Subsequently, a stretching and zooming method was proposed to obtain the training data. After training, the test and comparison results demonstrated the high accuracy rate of the proposed method and the improvement in recognition efficiency. The proposed method could also identify convex features, which further improved the recognition range.

Suggested Citation

  • Fangwei Ning & Yan Shi & Maolin Cai & Weiqing Xu, 2023. "Part machining feature recognition based on a deep learning method," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 809-821, February.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01827-7
    DOI: 10.1007/s10845-021-01827-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01827-7
    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/s10845-021-01827-7?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. Peizhi Shi & Qunfen Qi & Yuchu Qin & Paul J. Scott & Xiangqian Jiang, 2020. "A novel learning-based feature recognition method using multiple sectional view representation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1291-1309, June.
    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. Xinhua Yao & Di Wang & Tao Yu & Congcong Luan & Jianzhong Fu, 2023. "A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2599-2610, August.
    2. Shirine El Zaatari & Yuqi Wang & Yudie Hu & Weidong Li, 2022. "An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1503-1519, June.
    3. Maja Trstenjak & Tihomir Opetuk & Hrvoje Cajner & Natasa Tosanovic, 2020. "Process Planning in Industry 4.0—Current State, Potential and Management of Transformation," Sustainability, MDPI, vol. 12(15), pages 1-25, July.
    4. Victoria Miles & Stefano Giani & Oliver Vogt, 2023. "Recursive encoder network for the automatic analysis of STEP files," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 181-196, January.

    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:2:d:10.1007_s10845-021-01827-7. 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.