IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i1d10.1007_s10845-018-1442-7.html
   My bibliography  Save this article

Point-by-point prediction of cutting force in 3-axis CNC milling machines through voxel framework in digital manufacturing

Author

Listed:
  • Omid Yousefian

    (North Carolina State University)

  • Andrey Balabokhin

    (University of South Carolina)

  • Joshua Tarbutton

    (UNC Charlotte)

Abstract

A new digital-based model is presented for the prediction of cutting forces in 3-axis CNC milling of surfaces. The model uses an algorithm to detect the work-piece/cutter intersection domain automatically for given cutter location, cutter and work-piece geometries. The algorithm uses a voxel-based representation for the workpiece and rasterized tool slice to detect the tool engagement. Furthermore, an analytical approach is used to calculate the cutting forces based on the discretized model. The results of model validation experiments on machining PMMA, Aluminum 6061 and 304 Stainless Steel are presented. Comparisons of the predicted and measured forces show that this digital approach can be used to accurately predict forces during machining.

Suggested Citation

  • Omid Yousefian & Andrey Balabokhin & Joshua Tarbutton, 2020. "Point-by-point prediction of cutting force in 3-axis CNC milling machines through voxel framework in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 215-226, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1442-7
    DOI: 10.1007/s10845-018-1442-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-018-1442-7
    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/s10845-018-1442-7?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:31:y:2020:i:1:d:10.1007_s10845-018-1442-7. 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.