IDEAS home Printed from https://ideas.repec.org/a/aad/iseicj/v5y2017i0p1169-1174.html
   My bibliography  Save this article

Setup Generation Using Neural Networks

Author

Listed:
  • Oleg Mihaylov

    (Faculty of Industrial Technology, Technical University of Sofia, Bulgaria)

  • Galina Nikolcheva

    (Faculty of Industrial Technology, Technical University of Sofia, Bulgaria)

  • Peter Popov

    (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria)

Abstract

The article presents an unsupervised learning algorithm that groups technological features in a setup for machining process. Setup generation is one of the most important tasks in automated process planning and in fixture configuration. A setup is created based on approach direction of the features. The algorithm proposed in this work generates a neural network that determines the setup each feature belongs to, and the number of setups generated is minimal. This algorithm, unlike others, is not influenced by the order of the input sequence. Parallel implementation of the algorithm is straightforward and can significantly increase the computational performance.

Suggested Citation

  • Oleg Mihaylov & Galina Nikolcheva & Peter Popov, 2017. "Setup Generation Using Neural Networks," CBU International Conference Proceedings, ISE Research Institute, vol. 5(0), pages 1169-1174, September.
    • Handle: RePEc:aad:iseicj:v:5:y:2017:i:0:p:1169-1174
    DOI: 10.12955/cbup.v5.1090
    as

    Download full text from publisher

    File URL: https://ojs.journals.cz/index.php/CBUIC/article/view/1090/1649
    Download Restriction: no
    File URL: https://libkey.io/10.12955/cbup.v5.1090?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
    ---><---

    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:aad:iseicj:v:5:y:2017:i:0:p:1169-1174. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Petr Hájek (email available below). General contact details of provider: https://ojs.journals.cz/index.php/CBUIC .

    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.