IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v57y2019i8p2344-2355.html
   My bibliography  Save this article

An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners

Author

Listed:
  • Sue E. Stankus
  • Krystel K. Castillo-Villar

Abstract

Statistical quality control techniques are crucial for manufacturing companies with tight tolerances but high-volume data generated from laser scanners has pushed the limits of traditional control charts. In a previous work, multivariate generalised likelihood ratio control (MGLR) chart was used to identify process shifts and locate defects on artefacts by converting 3D point cloud data to a 2D image. This paper presents a 3D MGLR control chart that retains the 3D nature of the point cloud data and uses a Fourier transform of the point errors. The average run length (ARL1) of the proposed 3D MGLR was tested using a designed experiment with ten replications and varying the number of past scans and number of Regions of Interest (ROIs). The designed experiment was repeated using three defects: incorrect surface curvature, surface scratch, and surface dent. The proposed methodology identified the dent while the prior methodology never identified it. In addition, the proposed methodology had a significantly shorter ARL1 than the prior methodology for the scratch and no significant difference in the ARL1 for the incorrect surface curvature. The proposed 3D MGLR control chart enabled the usage of 3D data without needing to convert it to a 2D image.

Suggested Citation

  • Sue E. Stankus & Krystel K. Castillo-Villar, 2019. "An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2344-2355, April.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:8:p:2344-2355
    DOI: 10.1080/00207543.2018.1518600
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2018.1518600
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2018.1518600?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. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.

    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:57:y:2019:i:8:p:2344-2355. 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.