IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i7d10.1007_s10845-022-01931-2.html
   My bibliography  Save this article

Model predictive force control in milling based on an ensemble Kalman filter

Author

Listed:
  • Max Schwenzer

    (RWTH Aachen University)

  • Sebastian Stemmler

    (RWTH Aachen University)

  • Muzaffer Ay

    (RWTH Aachen University)

  • Adrian Karl Rüppel

    (RWTH Aachen University)

  • Thomas Bergs

    (RWTH Aachen University)

  • Dirk Abel

    (RWTH Aachen University)

Abstract

Process force determines productivity, quality, and safety in milling. Current approaches of process design often focus on a priori optimization. In order to enable online optimization, the establishment of active force controllers is required. Due to fast-changing engagement conditions of the tool in conjunction with the slower machine dynamics, classic control is not suited. A promising approach is the application of model predictive control (MPC) for force control, which is proposed in this contribution. The model predictive force controller (MPFC) explicitly takes into account a model to predict the immediate future. It consists of a model of the machine tool and a separate model of the process. The process model describes the relation between feed velocity of the tool, force, and geometric properties of the tool, such as the radial deviation, and of the tool/workpiece engagement. The feedback loop of the controller is closed by an online identification of the process model to account changes in the material properties or of the tool wear state. For this identification an ensemble Kalman filter (EnKF) is applied. The MPFC solves an optimization problem on the future behavior in each sampling step to determine the optimal controller output enabling high dynamic control. The proposed control system is validated experimentally and compared with a conventionally designed process with constant feed. It can be shown that the manufacturing time is reduced by 50%. The system enables a paradigm shift in the design of milling processes operating the manufacturing process at its technological limit.

Suggested Citation

  • Max Schwenzer & Sebastian Stemmler & Muzaffer Ay & Adrian Karl Rüppel & Thomas Bergs & Dirk Abel, 2022. "Model predictive force control in milling based on an ensemble Kalman filter," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1907-1919, October.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:7:d:10.1007_s10845-022-01931-2
    DOI: 10.1007/s10845-022-01931-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-01931-2
    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/s10845-022-01931-2?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. Xin Tong & Qiang Liu & Yinuo Zhou & Pengpeng Sun, 2025. "A digital twin-driven cutting force adaptive control approach for milling process," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 551-568, 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:33:y:2022:i:7:d:10.1007_s10845-022-01931-2. 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.