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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
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    References listed on IDEAS

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    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.
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