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Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: A case study

Author

Listed:
  • Yazid Saif
  • Yusri Yusof
  • Anika Zafiah M Rus
  • Atef M Ghaleb
  • Sobhi Mejjaouli
  • Sami Al-Alimi
  • Djamal Hissein Didane
  • Kamran Latif
  • Aini Zuhra Abdul Kadir
  • Hamood Alshalabi
  • Safwan Sadeq

Abstract

In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model’s surface through circular dimension analysis. This involves sending the model’s image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.

Suggested Citation

  • Yazid Saif & Yusri Yusof & Anika Zafiah M Rus & Atef M Ghaleb & Sobhi Mejjaouli & Sami Al-Alimi & Djamal Hissein Didane & Kamran Latif & Aini Zuhra Abdul Kadir & Hamood Alshalabi & Safwan Sadeq, 2023. "Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: A case study," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-25, October.
    • Handle: RePEc:plo:pone00:0292814
    DOI: 10.1371/journal.pone.0292814
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    References listed on IDEAS

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    1. Francisco G. Bulnes & Ruben Usamentiaga & Daniel F. Garcia & J. Molleda, 2016. "An efficient method for defect detection during the manufacturing of web materials," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 431-445, April.
    2. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
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