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

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

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Abstract

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%.

Item Type:	Article
Uncontrolled Keywords:	Computer vision, Image processing, CMM, 3SMVI, Inspection, IoT, Roundness
Divisions:	Faculty of Mechanical and Manufacturing Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	01 Jul 2024 14:31
Last Modified:	01 Jul 2024 14:31
URI:	http://eprints.utem.edu.my/id/eprint/27268
Statistic Details:	View Download Statistic

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