IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v39y2025i3-4-5p406-422.html
   My bibliography  Save this article

Fixture devices monitoring for machining condition optimisation aided by machine learning

Author

Listed:
  • Felipe Alves de Oliveira Perroni
  • Ugo Ibusuki
  • Eduardo de Senzi Zancul
  • Klaus Schützer
  • Cláudio Nogueira de Meneses
  • Thiago Cannabrava de Sousa

Abstract

This paper focuses on applying recent digitisation technologies for machining process improvement based on fixture device monitoring. Industry 4.0 technologies support smart monitoring of manufacturing processes, enabling semi-autonomous tool process parameters adjustment, reducing human-machine interactions, resulting in more accurate process improvements. The paper aims to present the results of a project development and validation of a machining conditioning monitoring system, combining measures conducted directly in the spindle unit and fixture devices. The machining condition monitoring system, aided by a machine learning algorithm, uses vibration data to determine the tool's maximum wear. The project, a collaborative effort by two universities, was designed for practical application and rigorously tested in a real-world operational environment at an automotive company.

Suggested Citation

  • Felipe Alves de Oliveira Perroni & Ugo Ibusuki & Eduardo de Senzi Zancul & Klaus Schützer & Cláudio Nogueira de Meneses & Thiago Cannabrava de Sousa, 2025. "Fixture devices monitoring for machining condition optimisation aided by machine learning," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 39(3/4/5), pages 406-422.
    • Handle: RePEc:ids:ijmtma:v:39:y:2025:i:3/4/5:p:406-422
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=145949
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijmtma:v:39:y:2025:i:3/4/5:p:406-422. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    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.