IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i6d10.1007_s10845-018-1404-0.html
   My bibliography  Save this article

Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations

Author

Listed:
  • Berend Denkena

    (Leibniz Universität Hannover)

  • Benjamin Bergmann

    (Leibniz Universität Hannover)

  • Matthias Witt

    (Leibniz Universität Hannover)

Abstract

New design concepts for high-performance components are part of the current research. Because of various material properties and chemical composition, the cutting characteristics and chip formation mechanisms change during the machining process. Thus, it can be mandatory to identify the material and adapt the process parameters during machining. As a result, the workpiece quality is optimized while increasing the tool life. Therefore, this paper investigates a new approach to determine the machined material in-process by machine-learning. A cylindrical turning process is performed for friction welded EN-AW6082/20MnCr5 and C22/41Cr4 shafts. Acceleration and process force signals as well as control signals are measured and monitoring features are generated. These features are ranked and selected based on the information value by the joint mutual information method. Afterwards, four machine-learning models are trained to identify the machined material based on the signal features. The monitoring quality is evaluated during various cylindrical turning processes and the most appropriate machine-learning algorithm is determined. Thus, a new methodology for in-process material identification in CNC turning machines based on signal analysis and machine-learning algorithm is proposed.

Suggested Citation

  • Berend Denkena & Benjamin Bergmann & Matthias Witt, 2019. "Material identification based on machine-learning algorithms for hybrid workpieces during cylindrical operations," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2449-2456, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1404-0
    DOI: 10.1007/s10845-018-1404-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1404-0
    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/s10845-018-1404-0?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benjamin Lutz & Dominik Kisskalt & Andreas Mayr & Daniel Regulin & Matteo Pantano & Jörg Franke, 2021. "In-situ identification of material batches using machine learning for machining operations," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1485-1495, June.

    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:6:d:10.1007_s10845-018-1404-0. 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.