REVIEW ON LOW-COST IMAGE PROCESSING SOLUTION FOR RIVET QUALITY INSPECTION
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Rivets are critical fastening elements used in aerospace, automotive, and construction industries where structural integrity is essential. Traditional inspection methods rely on manual techniques, which are slow, inconsistent, and prone to human error. This paper presents a low-cost automated rivet inspection system using image processing and computer vision techniques. The proposed system integrates Python-based image processing, Programmable Logic Controllers (PLC), and Internet of Things (IoT) technologies to enable real-time inspection and monitoring. The system improves inspection accuracy, reduces processing time, and provides an affordable solution for small and medium-scale industries.
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Sonar, O., Bagul, P., Taskar, R., Thakare, V., Zalte, A. & Malode, M. (2026). Review on Low-Cost Image Processing Solution for Rivet Quality Inspection. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.553
Sonar, Ojas, et al.. "Review on Low-Cost Image Processing Solution for Rivet Quality Inspection." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.553.
Sonar, Ojas,Pranav Bagul,Ruchita Taskar,Vaibhavi Thakare,Arun Zalte, and Madhuri Malode. "Review on Low-Cost Image Processing Solution for Rivet Quality Inspection." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.553.
References
[2] M. Aslam, T. M. Khan, S. S. Naqvi, G. Holmes, and R. Naffa, "On the application of automated machine vision for leather defect inspection and grading: A survey," IEEE Access, vol. 7, pp. 176065–176086, 2019.
[3] J. Bai et al., "A comprehensive survey on machine learning driven material defect detection," ACM Comput. Surv., vol. 57, pp. 1–36, 2025.
[4] F. Kashefinishaburi and S. V. Hoa, "A technique for real-time defect detection of composite material using machine learning and transfer learning," in Proc. American Society for Composites, 2022.
[5] M. Hussain, "Sustainable machine vision for Industry 4.0: A review of CNNs and hardware accelerators," AI, vol. 5, pp. 1324–1356, 2024.
[6] A. Sioma, "Vision system in product quality control systems," Appl. Sci., vol. 13, p. 751, 2023.
[7] M. Dziubek et al., "Exploring the ViDiDetect tool for automated defect detection," Appl. Sci., vol. 13, p. 11098, 2023.
[8] IEC 61131-3, "Programmable Controllers—Part 3: Programming Languages," 2013.
[9] D. Jancarczyk et al., "Comparative analysis of measurement tools in the Cognex D900 vision system," Appl. Sci., vol. 14, p. 8296, 2024.
[10] B. Kim, M. Shin, and S. Hwang, "Precision defect detection using line scan camera and deep learning," Appl. Sci., vol. 14, p. 12054, 2024.
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