IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v47y2015i2p109-124.html
   My bibliography  Save this article

Bayesian hierarchical modeling for monitoring optical profiles in low-E glass manufacturing processes

Author

Listed:
  • Li Zeng
  • Nan Chen

Abstract

Low-emittance (low-E) glass manufacturing has become an important sector of the glass industry for energy efficiency of such glasses. However, the quality control scheme in the current processes is rather primitive and advanced statistical quality control methods need to be developed. As the first attempt for this purpose, this article considers monitoring of optical profiles, which are typical quality measurements in low-E glass manufacturing. A Bayesian hierarchical approach is proposed for modeling the optical profiles, which conducts model selection and estimation in an integrated framework. The effectiveness of the proposed approach is validated in a numerical study, and its use in Phase I analysis of optical profiles is demonstrated in a case study. The proposed approach will lay a foundation for quality control and variation reduction in low-E glass manufacturing.

Suggested Citation

  • Li Zeng & Nan Chen, 2015. "Bayesian hierarchical modeling for monitoring optical profiles in low-E glass manufacturing processes," IISE Transactions, Taylor & Francis Journals, vol. 47(2), pages 109-124, February.
  • Handle: RePEc:taf:uiiexx:v:47:y:2015:i:2:p:109-124
    DOI: 10.1080/0740817X.2014.892230
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0740817X.2014.892230
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0740817X.2014.892230?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:47:y:2015:i:2:p:109-124. 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.