Authors : Kiran Ravindra Manole; Saurabh R. Prasad; Shrinivas A. Patil

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/ya3euve2

Scribd : https://tinyurl.com/4xwf9afk

DOI : https://doi.org/10.38124/ijisrt/26apr1519

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Industrial manufacturing environments often rely on manual job allocation and quality inspection processes, which can lead to inefficiencies, lack of accountability, and inconsistent product quality. To address these challenges, this paper proposes a Smart Industrial Job Verification and Allocation System that integrates RFID-based worker authentication with machine vision–based job quality verification. In the proposed system, each operator is assigned a unique RFID card that enables secure identification and controlled access to job distribution. Operators can request raw jobs through a keypad interface, and a conveyor-based mechanism dispenses the required number of jobs while maintaining traceability. After completing the assigned tasks, the operator returns the finished jobs, which are automatically inspected using a camera-based image processing system implemented using OpenCV. The captured images are analyzed to verify job quality and detect defects based on predefined parameters corresponding to different job types. The system architecture employs a Raspberry Pi for high-level processing and an Arduino Uno for real-time hardware control, ensuring efficient coordination between sensing, processing, and actuation components. All transactions, including operator identity, job count, timestamps, and inspection results, are logged in a digital database for monitoring and analysis. Experimental evaluation of the prototype demonstrates improved operational efficiency, enhanced traceability of manufacturing tasks, and reliable automated quality verification. The proposed system provides a cost-effective and scalable solution for smart manufacturing environments aligned with Industry 4.0 principles.

Keywords : RFID Authentication, Computer Vision, Automated Quality Inspection, Smart Factory, Manufacturing Process Monitoring

References :

1. Rahul D. Chavhan, Sachin U. Chavhan, Ganesh B. Chavan, Real Time Industrial Monitoring System (OMAMS), arXiv preprint ,2014, 1402, 2231-2803.
2. R. Kumar, O. Patil, K. Nath, K. Rohilla, KS Sangwan, Machine Vision and RFID-Based Real-Time Part Traceability in a Learning Factory, Procedia CIRP,2021,104,630–635, doi: 10.1016/j.procir.2021.11.106
3. M. Alidoost, M. A. Mahmoudi, and M. G. Zadeh, Optimization of Industrial Warehouse Using RFID and Image Processing, International Journal of Research and Development Organization (IJRDO), 2018, 4,1811-1968, doi: 10.53555/cse.v4i1.1835.
4. Berardinucci F, & Urgo, M., “Advanced Computer Vision for Industrial Safety: Indoor Human Worker Localization Using Deep Learning,  European Symposium on Artificial Intelligence in Manufacturing, Springer, 2024, 134–143, doi: 10.1007/978-3-031-86489-6_15.
5. W. Yang, G. Luo, and W. Li, Design and implementation of work‑in‑process management system based on RFID technology, International Conference on Internet and Distributed Computing Systems, 2016, 9864, 254–262, doi: 10.1007/978‑3‑319‑45940‑0_23.
6. Kadoura, A., and E. P. Small,Tracking Productivity in Real-time Using Computer Vision, IOP Conference Series: Materials Science and Engineering,2022,1218, 012041,doi: 1088/1757-899X/1218/1/012041
7. Wang, Chuang, Pingyu Jiang, Deep neural networks based order completion time prediction by using real‑time job shop RFID data, Journal of Intelligent Manufacturing,2019,30, 1303–1318, doi: 10.1007/s10845‑017‑1325‑3.
8. A. Akbari, S. Mirshahi , M. Hashemipour, Application of RFID system for the process control of distributed manufacturing system, IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE),2015, 497-501,
9. Aydos, Thiago F., and Joao CE Ferreira, RFID-based system for lean manufacturing in the context of Internet of Things, 2016 IEEE International Conference on Automation Science and Engineering (CASE),2016 , 1140-1145, doi : 10.1109/COASE.2016.7743533.
10. Akundi, Aditya, and Mark Reyna, A machine vision based automated quality control system for product dimensional analysis, Procedia Computer Science, 2021, 185, 127-134, doi : 10.1016/j.procs.2021.05.014.
11. Sharma, Heena, and N. M. Suri, Implementation of quality control tools and techniques in manufacturing industry for process improvement, International Research Journal of Engineering and Technology,2017,4(5), 1581–1585.
12. Li, Jingshan, Semyon M. Meerkov, and Liang Zhang, Production systems engineering: Problems, solutions, and applications, Annual Reviews in Control, 2010, 34(1), 73-88, doi: 10.1016/j.arcontrol.2010.02.003.
13. Park M &Jeong J, Design and implementation of machine vision-based quality inspection system in mask manufacturing process, Sustainability,2022, 14(10),6009,doi: 10.3390/su14106009
14. Y. Tao, Y. Zhang, C. Zhang, W. Luo, and X. Cheng, Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges, Ieee Access,2017, 4, 6505-6519, doi: 10.1109/ACCESS.2017.2783682.
15. J. A. Fortoul -Diaz, A. Morales-Velazquez, R. Rodriguez, and L. Valdez, A smart factory architecture based on Industry 4.0 technologies: Open-source software implementation, IEEE access 11 (2023), ,11, 101727-101749, doi: 10.1109/ACCESS.2023.3316116.
16. L. S. Goecks, J. Smith, and A. Kumar, Industry 4.0 and smart systems in manufacturing: Guidelines for the implementation of a smart statistical process control, Applied System Innovation 7.2 , 2024,24,525–585, doi: 10.3390/asi7020024.