IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v60y2022i17p5201-5216.html
   My bibliography  Save this article

Process and machine selection in sampling-based tolerance-cost optimisation for dimensional tolerancing

Author

Listed:
  • Martin Hallmann
  • Benjamin Schleich
  • Sandro Wartzack

Abstract

Tolerance-cost optimisation, i.e. using optimisation techniques for tolerance allocation, is frequently used to determine a cost-efficient tolerance design that can meet the stringent requirements on high-quality products. Besides various manufacturing aspects, the selection of available alternative machines and processes hold great potential for an early optimal process planning by identifying their best combination. Although machine/process selection by minimum cost and mixed-integer optimisation is often applied in theory and practice, their proper implementation in tolerance-cost optimisation based on sampling techniques for tolerance analysis, which can statistically consider various individual part tolerance distributions, has not been studied so far. With the aim to overcome this drawback, this article focuses on machine/process selection in sampling-based tolerance-cost optimisation for dimensional tolerances considering the respective machine characteristics of several machine options, e.g. process capabilities and manufacturing distributions. A comparative study proves that machine/process selection by mixed-integer optimisation leads to minimum total manufacturing costs since it covers the whole search space, including all technically feasible machine combinations and thus identifies the global cost minimum.

Suggested Citation

  • Martin Hallmann & Benjamin Schleich & Sandro Wartzack, 2022. "Process and machine selection in sampling-based tolerance-cost optimisation for dimensional tolerancing," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5201-5216, September.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:17:p:5201-5216
    DOI: 10.1080/00207543.2021.1951867
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2021.1951867
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2021.1951867?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:60:y:2022:i:17:p:5201-5216. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.