Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 04002
Number of page(s) 14
Section Digital Twins, IoT, and Smart Manufacturing Systems
DOI https://doi.org/10.1051/epjconf/202635404002
Published online 02 March 2026
  1. I. McKay, & A. Anbalagan, A FEA simulation study on impeller failures considering various materials. Materials Today: Proceedings, 80, 54–61 (2023). [Google Scholar]
  2. Anbalagan, A., Kauffman, M., & Long, T. (2024, June). Advancing Digital Twin Technology in Manufacturing: A Comprehensive Study on Data Capture and Simulation of End Mills. In International Manufacturing Science and Engineering Con-ference (Vol. 88100, p. V001T02A010). American Society of Mechanical Engineers. [Google Scholar]
  3. A. G. Frank, L. S. Dalenogare, & N. F. Ayala, Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26 (2019). https://doi.org/10.1016/j.ijpe.2019.01.004 [CrossRef] [Google Scholar]
  4. Hughes, L., Dwivedi, Y. K., Rana, N. P., Williams, M. D., & Raghavan, V. (2022). Perspectives on the future of manufacturing within the industry 4.0 era. Production Planning & Control, 33(2-3), 138–158. [Google Scholar]
  5. D. I. Lee, & H. C. Lim, Erosion-corrosion damages of water-pump impeller. International Journal of Automotive Technology, 10(5), 629–634 (2009). https://doi.org/10.1007/s12239-009-0074-5 [Google Scholar]
  6. A. Van Bennekom, F. Berndt, & M. N. Rassool, Pump impeller failures - A compendium of case studies. Engineering Failure Analysis, 8(2), 145–156 (2001). https://doi.org/10.1016/S1350-6307(99)00044-8 [Google Scholar]
  7. K. Alexander, B. Donohue, T. Feese, G. Vanderlinden, & M. Kral, Failure analysis of an MVR (mechanical vapor recompressor) impeller. Engineering Failure Analysis, 17(6), 1345–1358 (2010). https://doi.org/10.1016/j.engfailanal.2010.03.009 [Google Scholar]
  8. Z. W. Yu, X. L. Xu, J. Guo, & T. Cai, Fracture analysis of impeller blade of a locomotive draught-fan. Engineering Failure Analysis, 27, 16–29 (2013). https://doi.org/10.1016/j.engfailanal.2012.12.004 [Google Scholar]
  9. R. Wei, X. Chen, Z. Ai, & Y. Jin, Experimental study and numerical simulation on the SSCC in FV520B stainless steel exposed to H2S + Cl" environment. International Journal of Hydrogen Energy, 43(18), 9059–9067 (2018). https://doi.org/10.1016/j.ijhydene.2018.03.056 [Google Scholar]
  10. X. Zhang, W. Zhao, & Y. Xie, Fatigue failure analysis of semi-open impeller with mistuning considered. Engineering Failure Analysis, 95, 127–139 (2019). https://doi.org/10.1016/j.engfailanal.2018.09.014 [Google Scholar]
  11. X. V. Wang, & L. Wang, Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. International Journal of Production Research, 57(12), 3892–3902 (2019). https://doi.org/10.1080/00207543.2018.1497819 [Google Scholar]
  12. I. Castelo-Branco, F. Cruz-Jesus, & T. Oliveira, Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. Computers in Industry, 107, 22–32 (2019). https://doi.org/10.1016/j.compind.2019.01.007 [Google Scholar]
  13. D. Li, H. Tang, S. Wang, & C. Liu, A big data enabled load-balancing control for smart manufacturing of Industry 4.0. Cluster Computing, 20, 1855–1864 (2017). https://doi.org/10.1007/s10586-017-0891-0 [Google Scholar]
  14. M. Ehrlich, L. Wisniewski, & J. Jasperneite, State of the art and future applications of industrial wireless sensor networks. In Kommunikation und Bildverarbeitung in der Automation (pp. 28–39). Springer (2018). https://doi.org/10.1007/978-3-662-55232-2_3 [Google Scholar]
  15. J. H. Kim, A review of cyber-physical system research relevant to the emerging IT trends: Industry 4.0, IoT, big data, and cloud computing. Journal of Industrial Integration and Management, 2(3), 1750011 (2017). https://doi.org/10.1142/S2424862217500117 [Google Scholar]
  16. R. Y. Zhong, X. Xu, E. Klotz, & S. T. Newman, Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630 (2017). https://doi.org/10.1016/J.ENG.2017.05.015 [CrossRef] [Google Scholar]
  17. E. Oztemel, & S. Gursev, Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31, 127–182 (2020). https://doi.org/10.1007/s 10845-018-1433-8 [CrossRef] [Google Scholar]
  18. F. Tao, Q. Qi, L. Wang, & A. Y. C. Nee, Digital twins and cyber-physical systems toward smart manufacturing and Industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661 (2019). https://doi.org/10.1016/j.eng.2019.01.014 [CrossRef] [Google Scholar]
  19. M. Saqlain, M. Piao, Y. Shim, & J. Y. Lee, Framework of an IoT-based industrial data management for smart manufacturing. Journal of Sensor and Actuator Networks, 8(2), 25 (2019). https://doi.org/10.3390/jsan8020025 [Google Scholar]
  20. J. Wan, B. Chen, M. Imran, F. Tao, D. Li, C. Liu, & S. Ahmad, Toward dynamic resources management for IoT-based manufacturing. IEEE Communications Magazine, 56(2), 52–59 (2018). https://doi.org/10.1109/MCOM.2018.1700460 [Google Scholar]
  21. Oliveira, M., Chauhan, S., Pereira, F., Felgueiras, C., & Carvalho, D. (2023). Blockchain protocols and edge computing targeting industry 5.0 needs. Sensors, 23(22), 9174. [Google Scholar]
  22. Freitas, Luis, Marco Silva, Gabriel Vale, Camelia Avram, Helena Lopes, Filipe Pereira, Nuno Leal, and José Machado. "OPC UA and MQTT performance analysis within a unified namespace context." Internet of Things (2025): 101734. [Google Scholar]
  23. Freitas, Luis, Filipe Pereira, Helena Lopes, Camelia Avram, Nuno Leal, Teresa Morgado, and José Machado. "OPC-UA vs. MQTT (UNS): Evaluating Alignment with RAMI4. 0 Through Literature Review." In International Conference Innovation in Engineering, pp. 434–445. Cham: Springer Nature Switzerland, 2025. [Google Scholar]
  24. https://nodered.org/ (Date Last Accessed: 12/10/2025) [Google Scholar]
  25. Arduino. (n.d.). UNO R3. Arduino Documentation. Retrieved from https://docs.arduino.cc/hardware/unos-rev3/ [Google Scholar]
  26. https://plm.sw.siemens.com/en-US/insights-hub/ (Date Last Accessed: 02/11/2025) [Google Scholar]
  27. SparkFun. (n.d.). Piezo vibration sensor hookup guide. SparkFun Learn. Retrieved from https://learn.sparkfun.com/tutorials/piezo-vibration-sensor-hookup-guide/all [Google Scholar]

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