IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i7d10.1007_s10845-018-1426-7.html
   My bibliography  Save this article

Continuous improvement of HSM process by data mining

Author

Listed:
  • Victor Godreau

    (University of Nantes
    Europe Technologies)

  • Mathieu Ritou

    (University of Nantes)

  • Etienne Chové

    (Europe Technologies)

  • Benoit Furet

    (University of Nantes)

  • Didier Dumur

    (Centrale Supélec)

Abstract

The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach.

Suggested Citation

  • Victor Godreau & Mathieu Ritou & Etienne Chové & Benoit Furet & Didier Dumur, 2019. "Continuous improvement of HSM process by data mining," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2781-2788, October.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:7:d:10.1007_s10845-018-1426-7
    DOI: 10.1007/s10845-018-1426-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1426-7
    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/s10845-018-1426-7?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.

    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:30:y:2019:i:7:d:10.1007_s10845-018-1426-7. 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.

    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: 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.