IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v41y2009i9p804-818.html
   My bibliography  Save this article

Optimal sensor distribution in multi-station assembly processes for maximal variance detection capability

Author

Listed:
  • Yuan Ren
  • Yu Ding

Abstract

Recent advances in sensor technology now allow manufacturers to distribute multiple sensors in multi-station assembly processes. A distributed sensor system enables the continual monitoring of manufactured products and greatly facilitates the determination of the underlying process variation sources that cause product quality defects. This paper addresses the problem of optimally distributing sensors in a multi-station assembly process to achieve a maximal variance detection capability. A sensitivity index is proposed for characterizing the detection ability of process variance components and the optimization problem for sensor distribution is formulated for a multi-station assembly process. A data-mining-guided evolutionary method is devised to solve this non-linear optimization problem. The data-mining-guided method demonstrates a considerable improvement compared to the existing alternatives. Guidance on practical issues such as the interpretation of the rules generated by the data mining method and how many sensors are required are also provided.

Suggested Citation

  • Yuan Ren & Yu Ding, 2009. "Optimal sensor distribution in multi-station assembly processes for maximal variance detection capability," IISE Transactions, Taylor & Francis Journals, vol. 41(9), pages 804-818.
    • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:9:p:804-818
    DOI: 10.1080/07408170902789050
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07408170902789050
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07408170902789050?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:uiiexx:v:41:y:2009:i:9:p:804-818. 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/uiie .

    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.