IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i10p1550147719883551.html
   My bibliography  Save this article

Machine learning–based automated image processing for quality management in industrial Internet of Things

Author

Listed:
  • Nematullo Rahmatov
  • Anand Paul
  • Faisal Saeed
  • Won-Hwa Hong
  • HyunCheol Seo
  • Jeonghong Kim

Abstract

The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used technology for this is to use industrial image processing that is based on the use of special cameras or imaging systems installed within the production line. In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning–based approach is used for proper classification. This model not only focuses on just the abnormalities but also helps in configuring the angles from which images of the production are taken, and our methods show 92% accuracy.

Suggested Citation

  • Nematullo Rahmatov & Anand Paul & Faisal Saeed & Won-Hwa Hong & HyunCheol Seo & Jeonghong Kim, 2019. "Machine learning–based automated image processing for quality management in industrial Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
  • Handle: RePEc:sae:intdis:v:15:y:2019:i:10:p:1550147719883551
    DOI: 10.1177/1550147719883551
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719883551
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1550147719883551?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
    ---><---

    References listed on IDEAS

    as
    1. Anand Paul & Seungmin Rho, 2016. "Probabilistic Model for M2M in IoT networking and communication," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 62(1), pages 59-66, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anandkumar Balasubramaniam & Anand Paul & Won-Hwa Hong & HyunCheol Seo & Jeong Hong Kim, 2017. "Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities," Sustainability, MDPI, vol. 9(7), pages 1-12, June.

    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:sae:intdis:v:15:y:2019:i:10:p:1550147719883551. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (email available below). General contact details of provider: .

    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.