Digital Twin to Control and Monitor an Industrial Cyber-Physical Environment Supported by Augmented Reality
Source abstract
Abstract
Increasing industrial development and digital transformations have given rise to a technology called Digital Twin (DT) that has the potential to break the barrier between physical and cyberspace. DT is a virtual and dynamic model enabled through a bidirectional data flow that creates high-reliability models with interconnection and fusion between the physical and digital systems for full integration. In smart manufacturing, this technology is increasingly used in research and industry. However, the studies conducted do not provide a definition or a single integrally connected model. To develop the Digital Twin shown in this research, the literature was reviewed to learn about the enabling technologies and architectures used at the industrial level. Then, a methodology was used to obtain the physical process information, create the digital environment, communicate the physical environment, apply simulation models in the digital environment, and parameterize the simulation environment with the physical process in real-time to obtain the digital twin supported with augmented reality. The system was implemented in the MPS-500 modular production station that has industrial sensors and actuators. The virtual environment was designed with Blender and Vuforia to create the augmented reality environment. In the proposed methodology, robust devices (field and control level) and low-cost embedded systems were used for the creation and communication of the virtual environment (monitoring and control); for the joint work of these technologies, they were carried out through the use of the following protocols: Open Platform Communications United Architecture (OPC UA), Ethernet, and machine to machine (M2M), with which a communication was achieved between the different levels of the automation pyramid. The results show that the proposed methodology for the implementation of the DT allows bidirectional communication between the physical and virtual environment and can also be visualized with the support of AR, thus providing its characteristics to the proposed DT. Digital Twin is an essential factor in creating virtual environments and improving applications between the real and digital world, establishing a bidirectional communication through the Ethernet protocol, with a communication time of approximately 100 ms. This technology interacts with the virtual environment and performs mappings, thus achieving timely and dynamic adjustment. This improves data management and production and incorporates process simulation and physical control in real-time, allowing to execute and trigger actions in the physical equipment simultaneously.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento De Automática, Universidad Politécnica De Madrid, UPM, 28006 Madrid, Spain
Departamento De Electrónica, Universidad Politécnica Salesiana, UPS, Quito 170146, Ecuador
References (58)
Show source-provided references
1. Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison10.1016/j.eng.2019.01.014 ↗
2. Lin, W.D., and Low, M.Y.H. (2020, January 14–17). Concept design of a system architecture for a manufacturing cyber-physical digital twin system. Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore.10.1109/IEEM45057.2020.9309795 ↗
3. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison10.1109/ACCESS.2018.2793265 ↗
4. Digital twin-based industrial cloud robotics: Framework, control approach, and implementation10.1016/j.jmsy.2020.07.013 ↗
5. Intelligent Small Object Detection Based on Digital Twinning for Smart Manufacturing in Industrial CPS10.1109/TII.2021.3061419 ↗
6. The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.010.1016/j.procir.2016.11.152 ↗
7. Digital twin improved via visual question answering for vision-language interactive mode in human–machine collaboration10.1016/j.jmsy.2020.07.011 ↗
8. Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry10.1016/j.ijinfomgt.2019.05.020 ↗
9. A Review of the Roles of Digital Twin in CPS-based Production Systems10.1016/j.promfg.2017.07.198 ↗
10. Towards Smart Manufacturing Using Spiral Digital Twin Framework and Twinchain10.1109/TII.2020.3047840 ↗
11. Sjarov, M., Lechler, T., Fuchs, J., Brossog, M., Selmaier, A., Faltus, F., Donhauser, T., and Franke, J. (2020, January 8–11). The Digital Twin Concept in Industry—A Review and Systematization. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation, Vienna, Austria.10.1109/ETFA46521.2020.9212089 ↗
12. Revolution on digital twin technology—A patent research approach10.1007/s00170-020-05314-w ↗
13. Enablers and barriers to the implementation of digital twins in the process industry: A systematic literature review
14. Josifovska, K., Yigitbas, E., and Engels, G. (2019, January 28). Reference Framework for Digital Twins within Cyber-Physical Systems. Proceedings of the 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems SEsCPS, Montreal, QC, Canada.10.1109/SEsCPS.2019.00012 ↗
15. Nagy, B.G., Dóka, J., Rácz, S., Szabó, G., Pelle, I., Czentye, J., Toka, L., and Sonkoly, B. (2019, January 7–10). Towards human-robot collaboration: An industry 4.0 VR platform with clouds under the hood. Proceedings of the 2019 IEEE 27th International Conference on Network Protocols, Chicago, IL, USA.10.1109/ICNP.2019.8888107 ↗
16. A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation10.1016/j.rcim.2022.102515 ↗
17. A Digital Twin based Service Oriented Application for a 4.0 Knowledge Navigation in the Smart Factory
18. Jeon, S.M., and Schuesslbauer, S. (2020, January 14–17). Digital twin application for production optimization. Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore.10.1109/IEEM45057.2020.9309874 ↗
19. Sierla, S., Sorsamäki, L., Azangoo, M., Villberg, A., Hytönen, E., and Vyatkin, V. (2020). Towards semi-automatic generation of a steady state digital twin of a brownfield process plant. Appl. Sci., 10.10.3390/app10196959 ↗
20. Erol, T., Mendi, A.F., and Dogan, D. (2020, January 22–24). Digital Transformation Revolution with Digital Twin Technology. Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies ISMSIT 2020, Istanbul, Turkey.10.1109/ISMSIT50672.2020.9254288 ↗
21. Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges10.1109/ACCESS.2020.2998723 ↗
22. Integration of digital twin and deep learning in cyber-physical systems: Towards smart manufacturing10.1049/iet-cim.2020.0009 ↗
23. Biesinger, F., and Weyrich, M. (2019, January 23–26). The Facets of Digital Twins in Production and the Automotive Industry. Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy.10.1109/ICMECT.2019.8932101 ↗
24. A digital twin reference for mass personalization in industry 4.010.1016/j.procir.2020.04.023 ↗
25. Caiza, G., and Sanz, R. (2022, January 22–25). Digital Twin for Monitoring an Industrial Process Using Augmented Reality. Proceedings of the 2022 17th Iberian Conference on Information Systems and Technologies, Madrid, Spain.10.23919/CISTI54924.2022.9820356 ↗
26. Bottani, E., Vignali, G., and Tancredi, G.P.C. (2020, January 15–17). A digital twin model of a pasteurization system for food beverages: Tools and architecture. Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation ICE/ITMC, Cardiff, UK.10.1109/ICE/ITMC49519.2020.9198625 ↗
27. Assawaarayakul, C., Srisawat, W., Ayuthaya, S.D.N., and Wattanasirichaigoon, S. (2019, January 11–13). Integrate Digital Twin to Exist Production System for Industry 4.0. Proceedings of the TIMES-iCON 2019—2019 4th Technology Innovation Management and Engineering Science International Conference, Bangkok, Thailand.10.1109/TIMES-iCON47539.2019.9024430 ↗
28. Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., and Loksik, M. (2017, January 6–9). The digital twin of an industrial production line within the industry 4.0 concept. Proceedings of the 2017 21st International Conference on Process Control PC, Pleso, Slovakia.10.1109/PC.2017.7976223 ↗
29. Architecture for Digital Twin implementation focusing on Industry 4.010.1109/TLA.2020.9082917 ↗
30. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues10.1016/j.rcim.2019.101837 ↗
31. The connotation of the digital twin, and the construction and application method of shop-floor digital twin10.1016/j.rcim.2020.102075 ↗
32. Hao, Q., Hongwei, W., and Yu, Z. (2021, January 5–7). Constructing Digital Twin for Smart Manufacturing. Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Dalian, China.
33. Decision-making in a fast fashion company in the Industry 4.0 era: A Digital Twin proposal to support operational planning10.1007/s00170-021-07543-z ↗
34. MES-integrated digital twin frameworks10.1016/j.jmsy.2020.05.007 ↗
35. Use of simulation in the industry 4.0 context: Creation of a Digital Twin to optimise decision making on non-automated process10.1080/17477778.2020.1811172 ↗
36. C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems10.1109/ACCESS.2017.2657006 ↗
37. Berezovskyi, A., Inam, R., El-Khoury, J., Torngren, M., and Fersman, E. (2019, January 22–25). Efficient state update exchange in a CPS environment for linked data-based digital twins. Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics, Helsinki, Finland.10.1109/INDIN41052.2019.8972284 ↗
38. FMU-supported simulation for CPS digital twin10.1016/j.promfg.2018.12.033 ↗
39. Adaptive Federated Learning and Digital Twin for Industrial Internet of Things10.1109/TII.2020.3034674 ↗
40. Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning10.1109/TII.2020.3019788 ↗
41. Digital Twin in manufacturing: A categorical literature review and classification
42. Xia, L., Lu, J., and Zhang, H. (2020, January 22–25). Research on Construction Method of Digital Twin Workshop Based on Digital Twin Engine. Proceedings of the 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications AEECA, Dalian, China.10.1109/AEECA49918.2020.9213649 ↗
43. Digital twin: Values, challenges and enablers from a modeling perspective10.1109/ACCESS.2020.2970143 ↗
44. Digital Twin: Enabling Technologies, Challenges, and Open Research10.1109/ACCESS.2020.2998358 ↗
45. A Requirements Driven Digital Twin Framework: Specification and Opportunities10.1109/ACCESS.2020.3000437 ↗
46. Augmented reality and digital twin system for interaction with construction machinery10.1080/13467581.2020.1869557 ↗
47. Flexible robotic teleoperation architecture for intelligent oil fields10.1016/j.heliyon.2020.e03833 ↗
48. Stary, C. (2021). Digital twin generation: Re-conceptualizing agent systems for behavior-centered cyber-physical system development. Sensors, 21.10.3390/s21041096 ↗
49. Digital twins: Artificial intelligence and the IoT cyber-physical systems10.1007/s41315-021-00180-5 ↗
50. Guo, D., Ling, S., Li, H., Ao, D., Zhang, T., Rong, Y., and Huang, G.Q. (2020, January 20–21). A framework for personalized production based on digital twin, blockchain and additive manufacturing in the context of Industry 4.0. Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering, Hong Kong.10.1109/CASE48305.2020.9216732 ↗
51. Empowering Digital Twin for Industry 4.0 using metaheuristic optimization algorithms: Case study PCB drilling optimization10.1007/s00170-021-06649-8 ↗
52. Digital twins: Development and implementation challenges within Moroccan context10.1007/s42452-020-2691-6 ↗
53. Preuveneers, D., Joosen, W., and Ilie-Zudor, E. (2018, January 16–19). Robust Digital Twin Compositions for Industry 4.0 Smart Manufacturing Systems. Proceedings of the IEEE International Enterprise Distributed Object Computing Workshop EDOCW, Stockholm, Sweden.10.1109/EDOCW.2018.00021 ↗
54. Raza, M., Kumar, P.M., Hung, D.V., Davis, W., Nguyen, H., and Trestian, R. (2020, January 11–13). A Digital Twin Framework for Industry 4.0 Enabling Next-Gen Manufacturing. Proceedings of the ICITM 2020—2020 9th International Conference on Industrial Technology and Management, Oxford, UK.10.1109/ICITM48982.2020.9080395 ↗
55. The Core of the Fourth Revolutions: Industrial Internet of Things, Digital Twin, and Cyber-Physical Loops10.1007/s10921-021-00777-7 ↗
56. Kamath, V., Morgan, J., and Ali, M.I. (2020, January 3). Industrial IoT and Digital Twins for a Smart Factory: An open source toolkit for application design and benchmarking. Proceedings of the GIoTS 2020—Global Internet of Things Summit, Dublin, Ireland.10.1109/GIOTS49054.2020.9119497 ↗
57. Caiza, G., Bonilla-Vasconez, P., Garcia, C.A., and Garcia, M.V. (2020, January 8–11). Augmented Reality for Robot Control in Low-cost Automation Context and IoT. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.10.1109/ETFA46521.2020.9212056 ↗
58. Wärmefjord, K., Söderberg, R., Schleich, B., and Wang, H. (2020). Digital twin for variation management: A general framework and identification of industrial challenges related to the implementation. Appl. Sci., 10.10.3390/app10103342 ↗