## 8 Conclusions and Future Research  
The fusion of IoT architecture, computer vision, and image processing methods has driven progress in measuring roundness within industrial automation. This integration enhances the accuracy and effectiveness of assessing roundness in industrial applications. IoT architecture facilitates real-time data exchange among devices, enabling instant monitoring and analysis of roundness measurements. This interconnectedness enables automated systems to gather and process image data from diverse sensors and cameras, offering a holistic perspective on an object's roundness. This paper discusses the development of an intelligent system utilizing machine vision inspection within an IoT framework and ISO 14649 interpreted files. A case study, including Example 1, Part 21, of STEP-NC, validated the system's functionality and confirmed its high accuracy compared to previous models. The article stresses the significance of assessing uncertainty from the perspective of the MVIS system rather than solely focusing on algorithm development. The 3 SMVI system, built on IoT architecture, has been fully developed, and the next step involves integrating advanced intelligent algorithms to enhance its capabilities. The primary contribution of this research is the examination of workpiece roundness using the 3 SMVI system within an IoT and CMM framework. The IoT architecture facilitates seamless communication between the roundness measurement system and connected devices, enabling efficient data exchange, remote monitoring, and data-driven decision-making. Computer vision algorithms play a crucial role in extracting meaningful information from the captured images. These algorithms can identify and analyze geometric features, enabling accurate roundness measurements. Through the use of edge detection, contour analysis, and shape-fitting algorithms, the system can precisely determine the roundness of objects with minimal human intervention. A vision system was introduced to measure and inspect circular parts' roundness errors using a non-contact method that adheres to geometric roundness tolerance standards. The 3 SMVI system was developed to assist measurement and inspection processes by analyzing captured images using image processing and computer vision algorithms based on Open Computer Vision. Therefore, to ensure the system's accuracy, the roundness error of holes in workpiece parts was measured using both a coordinate measuring machine (CMM) and the machine vision inspection system (MVIS). The results were compared, and the maximum difference between the outcomes of the two systems was found to be 8.7%. In a future study, we aim to enhance the 3 SMVI system's performance by upgrading hardware and software algorithms, improving image quality, and using more protocols for  
Figure 13: The difference error based on 3 SMVI and CMM in \(\upmu\)m.  
information transmission via cloud computing servers. It will focus on enhancing surface feature detection and manufacturing optimization using deep learning techniques and incorporating machine learning and artificial intelligence for continuous improvement and intelligent decision-making in industrial automation. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app132011419/s1](https://www.mdpi.com/article/10.3390/app132011419/s1)]([https://www.mdpi.com/article/10.3390/app132011419/s1](https://www.mdpi.com/article/10.3390/app132011419/s1)). Conceptualization, Y.S., A.Z.M.R., A.A. and H.Q.A.A. ; Methodology, Y.S., A.Z.M.R., Y.Y., M.L.A., S.A.-A. and A.A.; Software, Y.S. ; Validation, M.L.A., S.A.-A., D.H.D., M.A.A.-m. and H.Q.A.A. ; Formal analysis, M.L.A., S.A.-A., D.H.D. and M.A.A.-m.; Investigation, Y.Y., S.A.-A., D.H.D., A.A., Y.H.G. and H.Q.A.A. ; Resources, A.Z.M.R., A.A. and M.A.A.-m.; Data curation, Y.S. and M.A.A.-m.; Writing--original draft, Y.S. ; Writing--review & editing, A.Z.M.R., Y.Y., M.L.A. and Y.H.G. ; Supervision, A.Z.M.R. ; Funding acquisition, Y.H.G. and H.Q.A.A.