IOT-ENABLED CYBER-PHYSICAL ASSEMBLY SYSTEM FOR SEAL DETECTION IN ELECTRIC VEHICLE CHARGING CONNECTORS
Abstract
The reliability of electric vehicle charging connectors depends on correct seal installation, since the seal protects the interface from dust and moisture. This study developed an IoT-enabled cyber-physical assembly system integrating fiber-optic and photoelectric sensors, a programmable logic controller, pneumatic actuation, a Raspberry Pi edge gateway, and a human-machine interface. The system was evaluated on an industrial production line using the same connector model, machine, operating procedure, and inspection criteria as the baseline. The baseline rejection rate, drawn from routine production records before implementation, was 19.18%. After calibration, 600 consecutive connectors were assembled during a continuous five-hour validation run, with manual inspection by experienced quality personnel serving as the reference standard. The post-implementation rejection rate fell to 2.80%, an 85.41% relative reduction (16.38 percentage points), yielding a production yield of 97.17%. The detection system achieved 99.50% accuracy, 88.89% precision, 94.12% recall, 99.66% specificity, and a 91.43% F1-score. Average cycle time was 30 seconds per unit, with a repeatability error of ±0.10 mm across 30 repeated press-depth measurements. These results indicate improved defect prevention, assembly consistency, and traceability under the evaluated conditions, with remaining defects mostly outside the seal-sensing scope.
Full text article
References
Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160. https://doi.org/10.1109/ACCESS.2018.2870052
Andronie, M., L?z?roiu, G., ?tef?nescu, R., U??, C., & Dijm?rescu, I. (2021). Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability, 13(10), 5495. https://doi.org/10.3390/su13105495
Bankar, V. R., & Nandurkar, K. N. (2023). Implementation of IoT technology for quality improvement in an automotive industry. Materials Today: Proceedings, 118, 160-166. https://doi.org/10.1016/j.matpr.2023.03.485
Bokrantz, J., Skoogh, A., Berlin, C., Stahre, J., & Hansson, L. (2017). Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics, 191, 154-169. https://doi.org/10.1016/j.ijpe.2017.06.010
Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1-12. https://doi.org/10.1016/j.compind.2018.04.015
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. da P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Castillo, M., Monroy, R., & Ahmad, R. (2024). A cyber-physical production system for autonomous part quality control in polymer additive manufacturing material extrusion process. Journal of Intelligent Manufacturing, 35(8), 3655-3679. https://doi.org/10.1007/s10845-024-02389-0
Colombo, A. W., Karnouskos, S., Kaynak, O., Shi, Y., & Yin, S. (2017). Industrial cyberphysical systems: A backbone of the fourth industrial revolution. IEEE Industrial Electronics Magazine, 11(1), 6-16. https://doi.org/10.1109/MIE.2017.2648857
Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020). Visual-based defect detection and classification approaches for industrial applications: A survey. Sensors, 20(5), 1459. https://doi.org/10.3390/s20051459
Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23. https://doi.org/10.1109/5.554205
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36-52. https://doi.org/10.1016/j.cirpj.2020.02.002
Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28-44. https://doi.org/10.1016/j.inffus.2011.08.001
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1-2), 508-517. https://doi.org/10.1080/00207543.2017.1351644
Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
Lee, J., Noh, S. D., Kim, H. J., & Kang, Y. S. (2018). Implementation of cyber-physical production systems for quality prediction and operation control in metal casting. Sensors, 18(5), 1428. https://doi.org/10.3390/s18051428
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. https://doi.org/10.1016/j.jii.2017.04.005
Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia CIRP, 17, 9-13. https://doi.org/10.1016/j.procir.2014.03.115
Moosavi, S., Farajzadeh-Zanjani, M., Razavi-Far, R., Palade, V., & Saif, M. (2024). Explainable AI in manufacturing and industrial cyber-physical systems: A survey. Electronics, 13(17), 3497. https://doi.org/10.3390/electronics13173497
Neal, A. D., Sharpe, R. G., van Lopik, K., Tribe, J., Goodall, P., Lugo-Sanudo, H., Segura-Velandia, D., Conway, P., Jackson, L., Jackson, T., & West, A. (2021). The potential of Industry 4.0 cyber physical system to improve quality assurance: An automotive case study for wash monitoring of returnable transit items. CIRP Journal of Manufacturing Science and Technology, 32, 461-475. https://doi.org/10.1016/j.cirpj.2020.07.002
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. https://doi.org/10.1109/TPAMI.2016.2577031
Ryalat, M., ElMoaqet, H., & AlFaouri, M. (2023). Design of a smart factory based on cyber-physical systems and internet of things towards Industry 4.0. Applied Sciences, 13(4), 2156. https://doi.org/10.3390/app13042156
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646. https://doi.org/10.1109/JIOT.2016.2579198
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., & Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14(11), 4724-4734. https://doi.org/10.1109/TII.2018.2852491
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405-2415. https://doi.org/10.1109/TII.2018.2873186
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37, 517-527. https://doi.org/10.1016/j.jmsy.2015.04.008
Wang, S., & Jiao, R. J. (2024). Smart in-process inspection in human-cyber-physical manufacturing systems: A research proposal on human-automation symbiosis and its prospects. Machines, 12(12), 873. https://doi.org/10.3390/machines12120873
Weiss, E., Caplan, S., Horn, K., & Sharabi, M. (2024). Real-time defect detection in electronic components during assembly through deep learning. Electronics, 13(8), 1551. https://doi.org/10.3390/electronics13081551
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: Advantages, challenges, and applications. Production & Manufacturing Research, 4(1), 23-45. https://doi.org/10.1080/21693277.2016.1192517
Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018). A survey on industrial internet of things: A cyber-physical systems perspective. IEEE Access, 6, 78238-78259. https://doi.org/10.1109/ACCESS.2018.2884906
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941-2962. https://doi.org/10.1080/00207543.2018.1444806
Xie, X. (2008). A review of recent advances in surface defect detection using texture analysis techniques. Image and Vision Computing, 26(5), 621-633. https://doi.org/10.1016/j.imavis.2007.12.001
Yousef, N., Sata, A., Shukla, M., Jarboui, S., & Mobarsa, D. (2025). Blockchain-integrated IoT device for advanced inspection of casting defects. Scientific Reports, 15, 5300. https://doi.org/10.1038/s41598-025-86777-3
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616-630. https://doi.org/10.1016/J.ENG.2017.05.015
Authors
Copyright (c) 2026 Yuliadi Erdani, Susetyo Bagas Bhaskoro, Ahmad Fahrurozi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.