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Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques

In the era of Industry 4.0, the digital capture of products has become a critical aspect, which prompts the need for reliable inspection methods. In the current technological landscape, the Internet of Things (IoT) holds significant value, especially for industrial devices that require seamless comm...

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Main Authors: Mohd Rus, Anika Zafiah, Ahmed, Maznah Lliyas, Saif, Yazid, Yusof, Yusri, Al-Alimi, Sami, Didane, Djamal Hissein, Chi Adam, Mohd Khairil Anbia, Yeong, Hyeon Gu, Al-masni, Mohammed A., Abdulrab, Hakim Qaid Abdullah
Format: Article
Language:English
Published: MDPI 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27268/2/0269731122023648.PDF
http://eprints.utem.edu.my/id/eprint/27268/
https://www.mdpi.com/2076-3417/13/20/11419
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spelling my.utem.eprints.272682024-07-01T14:31:53Z http://eprints.utem.edu.my/id/eprint/27268/ Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques Mohd Rus, Anika Zafiah Ahmed, Maznah Lliyas Saif, Yazid Yusof, Yusri Al-Alimi, Sami Didane, Djamal Hissein Chi Adam, Mohd Khairil Anbia Yeong, Hyeon Gu Al-masni, Mohammed A. Abdulrab, Hakim Qaid Abdullah In the era of Industry 4.0, the digital capture of products has become a critical aspect, which prompts the need for reliable inspection methods. In the current technological landscape, the Internet of Things (IoT) holds significant value, especially for industrial devices that require seamless communication with local and cloud computing servers. This research focuses on the advancements made in roundness measurement techniques for industrial automation by leveraging an IoT architecture, computer vision, and image processing. The interconnectedness enables the efficient collection of feedback information, meeting the demands of closed-loop manufacturing. The accuracy and performance of assemblies heavily rely on the roundness of specific workpiece components. In order to address this problem, automated inspection methods are needed. A new method of computer vision for measuring and inspecting roundness is proposed in this paper. This method uses a non-contact method that takes into account all points on the contours of measured objects, making it more accurate and practical than conventional methods. The system developed by AMMC Laboratory captures Delrin work images and analyzes them using a specially designed 3SMVI system based on Open CV with Python script language. The system can measure and inspect several rounded components in the same part, including external frames and internal holes. It is calibrated to accommodate various units of measurement and has been tested using sample holes within the surface feature of the workpiece. According to the results of both techniques, there is a noticeable difference ranging from 2.9 µm to 11.6 µm. However, the accuracy of the measurements can be enhanced by utilizing a high-resolution camera with proper lighting. The results were compared to those obtained using a computer measurement machine (CMM), with a maximum difference of 8.7%. MDPI 2023-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27268/2/0269731122023648.PDF Mohd Rus, Anika Zafiah and Ahmed, Maznah Lliyas and Saif, Yazid and Yusof, Yusri and Al-Alimi, Sami and Didane, Djamal Hissein and Chi Adam, Mohd Khairil Anbia and Yeong, Hyeon Gu and Al-masni, Mohammed A. and Abdulrab, Hakim Qaid Abdullah (2023) Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques. Applied Sciences, 13 (20). pp. 1-22. ISSN 2076-3417 https://www.mdpi.com/2076-3417/13/20/11419 10.3390/app132011419
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description In the era of Industry 4.0, the digital capture of products has become a critical aspect, which prompts the need for reliable inspection methods. In the current technological landscape, the Internet of Things (IoT) holds significant value, especially for industrial devices that require seamless communication with local and cloud computing servers. This research focuses on the advancements made in roundness measurement techniques for industrial automation by leveraging an IoT architecture, computer vision, and image processing. The interconnectedness enables the efficient collection of feedback information, meeting the demands of closed-loop manufacturing. The accuracy and performance of assemblies heavily rely on the roundness of specific workpiece components. In order to address this problem, automated inspection methods are needed. A new method of computer vision for measuring and inspecting roundness is proposed in this paper. This method uses a non-contact method that takes into account all points on the contours of measured objects, making it more accurate and practical than conventional methods. The system developed by AMMC Laboratory captures Delrin work images and analyzes them using a specially designed 3SMVI system based on Open CV with Python script language. The system can measure and inspect several rounded components in the same part, including external frames and internal holes. It is calibrated to accommodate various units of measurement and has been tested using sample holes within the surface feature of the workpiece. According to the results of both techniques, there is a noticeable difference ranging from 2.9 µm to 11.6 µm. However, the accuracy of the measurements can be enhanced by utilizing a high-resolution camera with proper lighting. The results were compared to those obtained using a computer measurement machine (CMM), with a maximum difference of 8.7%.
format Article
author Mohd Rus, Anika Zafiah
Ahmed, Maznah Lliyas
Saif, Yazid
Yusof, Yusri
Al-Alimi, Sami
Didane, Djamal Hissein
Chi Adam, Mohd Khairil Anbia
Yeong, Hyeon Gu
Al-masni, Mohammed A.
Abdulrab, Hakim Qaid Abdullah
spellingShingle Mohd Rus, Anika Zafiah
Ahmed, Maznah Lliyas
Saif, Yazid
Yusof, Yusri
Al-Alimi, Sami
Didane, Djamal Hissein
Chi Adam, Mohd Khairil Anbia
Yeong, Hyeon Gu
Al-masni, Mohammed A.
Abdulrab, Hakim Qaid Abdullah
Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
author_facet Mohd Rus, Anika Zafiah
Ahmed, Maznah Lliyas
Saif, Yazid
Yusof, Yusri
Al-Alimi, Sami
Didane, Djamal Hissein
Chi Adam, Mohd Khairil Anbia
Yeong, Hyeon Gu
Al-masni, Mohammed A.
Abdulrab, Hakim Qaid Abdullah
author_sort Mohd Rus, Anika Zafiah
title Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
title_short Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
title_full Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
title_fullStr Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
title_full_unstemmed Advancements in roundness measurement parts for industrial automation using Internet of Things architecture-based computer vision and image processing techniques
title_sort advancements in roundness measurement parts for industrial automation using internet of things architecture-based computer vision and image processing techniques
publisher MDPI
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27268/2/0269731122023648.PDF
http://eprints.utem.edu.my/id/eprint/27268/
https://www.mdpi.com/2076-3417/13/20/11419
_version_ 1804070307224354816
score 13.211869

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