TY  - JOUR
TI  - Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques
ID  - scholars18134
N1  - cited By 1
N2  - 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. © 2023 by the authors.
IS  - 20
AV  - none
VL  - 13
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184846271&doi=10.3390%2fapp132011419&partnerID=40&md5=e4affe9d70abe10c4879aed77f088b76
A1  - Saif, Y.
A1  - Rus, A.Z.M.
A1  - Yusof, Y.
A1  - Ahmed, M.L.
A1  - Al-Alimi, S.
A1  - Didane, D.H.
A1  - Adam, A.
A1  - Gu, Y.H.
A1  - Al-masni, M.A.
A1  - Abdulrab, H.Q.A.
JF  - Applied Sciences (Switzerland)
Y1  - 2023///
ER  -