AN INDUSTRIAL APPLICATION OF DIGITAL TWIN FOR A SMART FACTORY MODEL USING COPPELIASIM

Year 2024, Volume: 8 Issue: 3, 316 - 325, 30.12.2024

Cüneyt Kılıç , Kamil Çetin , Mehmet Erdal Özbek

https://doi.org/10.46519/ij3dptdi.1452734

Abstract

A digital twin is quickly becoming a necessary component of manufacturing. When combined with Internet of Things (IoT) technology, it is possible to fulfil Industry 4.0's requirement for a digital transformation to create a smart factory. Many IoT devices, such as sensors, detect physical phenomena in their environments, collect data, and communicate. Digital twins that mimic the features and capabilities of real physical IoT devices can also be realized. Digitalization using digital twin requires not only IoT sensors but also software tools for virtualization. However, there are limited number of practical applications for the digitalization of large and complex systems. In this work, a smart factory model is used to develop its digital twin. Because the simulation software of industrial automation system manufacturers is licensed and different systems are incompatible with each other for digital twin, CoppeliaSim digital twin software, which is open source and can work independently of the industrial automation system model, was used in this study. The corresponding physical sensor data is transferred to CoppeliaSim in real time. Moreover, three scenarios were realized by achieving bilateral data transmission and control between the physical and digital twin models. Instant status monitoring presents the performance of digital twinning. While most of the digital twin studies in the literature are carried out either by transferring data only from the virtual system to the physical system or only from the physical system to the virtual system, in this study, simultaneous data exchange was implemented between the real and virtual systems.

Keywords

Digital Twin, Internet of Things, Smart Factory, Industry 4.0, CoppeliaSim

Ethical Statement

The authors declare that they have no conflict of interest.

Supporting Institution

This study was supported by TÜBİTAK with the project number 123E288.

Project Number

TÜBİTAK 123E288

Thanks

The authors would like to thank İzmir Kâtip Çelebi University Smart Factory Systems Application and Research Center (AFSUAM) and Kur Mermer San. ve Tic. A.S.

References

• 1. Kumas, E., & Erol, S., "Endustri 4.0’da Anahtar Teknoloji Olarak Dijital Ikizler", ", Politeknik Dergisi, Vol. 24, Issue 2, Pages 691-701, 2021.
• 2. Kalsoom, T., Ahmed, S., Rafi-ul-Shan, P. M., Azmat, M., Akhtar, P., Pervez, Z., & Ur-Rehman, M., "Impact of IoT on Manufacturing Industry 4.0: A new triangular systematic review", Sustainability, Vol. 13, Issue 22, Pages 12506, 2021.
• 3. Dilibal, S., and Şahin, H., "Isbirlikci endüstriyel robotlar ve dijital endustri.", International Journal of 3D Printing Technologies and Digital Industry Vol. 2, Issue 1, 86-96, 2018. 4. Tao, F., Zhang, H., Liu, A., & Nee, A. Y., "Digital twin in industry: State-of-the-art", IEEE Transactions on industrial informatics, Vol. 15, Issue 4, Pages 2405-2415, 2018.
• 5. Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P., "Digital twin modeling", Journal of Manufacturing Systems, Vol. 64, Issue 1, Pages, 372-389, 2022.
• 6. Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A., "Digital twins: State of the art theory and practice, challenges, and open research questions", Journal of Industrial Information Integration, Vol. 30, Issue -, Pages 100383, 2022.
• 7. Liu, M., Fang, S., Dong, H., & Xu, C, "Review of digital twin about concepts, technologies, and industrial applications", Journal of Manufacturing Systems,Vol. 58, Issue 1, Pages 346-361, 2021.
• 8. Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X., "Digital twins-based smart manufacturing system design in Industry 4.0: A review", Journal of manufacturing systems, Vol. 60, Issue 1, Pages 119-137, 2021.
• 9. Park, K. T., Son, Y. H., Ko, S. W., & Noh, S. D., "Digital twin and reinforcement learning-based resilient production control for micro smart factory", Applied Sciences, Vol. 11, Issue 7, Pages 2977, 2021.
• 10.Chancharoen, R., Chaiprabha, K., Wuttisittikulkij, L., Asdornwised, W., Saadi, M., & Phanomchoeng, G., "Digital twin for a collaborative painting robot, Sensors", Vol. 23, Issue. 1, Pages 17, 2022.
• 11. Coppelia Robotics "Robot simulator CoppeliaSim: create, compose, simulate, any ...", https://www.coppeliarobotics.com/, February 10, 2024.
• 12. Ren J., Nalpantidis, L. And Andersen N. A. &. Ravn., O. "Building Digital Twin of Mobile Robotics Testbed Using Centralized Localization System," in 11th International Conference on Control, Mechatronics and Automation (ICCMA), Pages 139-145, Grimstad, 2023.
• 13.Open Robotics, "Gazebo", https://gazebosim.org/home, January 20, 2024.
• 14. Stączek, P., Pizoń, J., Danilczuk, W., & Gola, A., A digital twin approach for the improvement of an autonomous mobile robots (AMR’s) operating environment-A case study. Sensors, Vol. 21, Issue 23, Pages 7830, 2021.
• 15. Al-Geddawy, T., "A digital twin creation method for an opensource low-cost changeable learning factory," Procedia Manufacturing, vol. 51, no. 1, Pages 1799-1805, 2020.
• 16. RoboDK Inc., "RoboDK: Simulator for industrial robots and offline programming", https://robodk.com/, October 15, 2023.
• 17. Pires, F., Cachada, A., Barbosa, J., Moreira, A. P. and P. Leitão, "Digital Twin in Industry 4.0: Technologies, Applications and Challenges," in IEEE 17th International Conference on Industrial Informatics (INDIN), Pages 721-726 Helsinki, Finland, 2019.
• 18. Dassault Systèmes, " SOLIDWORKS | 3D CAD Design Software & PDM Systems", https://www.solidworks.com/, February 21, 2024.
• 19. AutoDesk, "AutoCAD", https://www.autodesk.com/, February 21, 2024.
• 20. Trimble Inc., "SketchUp: 3D Design Software | 3D Modeling on the Web", https://www.sketchup.com/, January 25, 2024.
• 21. Magrin, C. E., Del Conte, G. And Todt, E., "Creating a digital twin as an open-source learning tool for mobile robotics," in Latin American Robotics Symposium (LARS), Brazilian Symposium on Robotics (SBR), and Workshop on Robotics in Education (WRE), Pages 13-18, Natal, 2021.
• 22. MathWorks, "Mathematical computing software for engineers and scientists", https://www.mathworks.com/, February 28, 2024.
• 23. PUC-Rio, "The Programming Language Lua", https://www.lua.org/, February 28, 2024.
• 24. Eaton, J. W. "GNU Octave ", https://octave.org/, February 22, 2024.
• 25. Rohmer, E., Singh, S. P., & Freese, M., "V-REP: A versatile and scalable robot simulation framework," in IEEE/RSJ international conference on intelligent robots and systems, Pages 1321-1326, Tokyo, 2013.
• 26. Nascimento, F. H., Cardoso, S. A., Lima, A. M. and Santos, D. F., "Synchronizing a collaborative arm’s digital twin in real-time," in Latin American Robotics Symposium (LARS), Brazilian Symposium on Robotics (SBR), and Workshop on Robotics in Education (WRE), Pages 230-235, Salvador, 2023.
• 27. Richard Hu, "HslCommunication", https://pypi.org/project/HslCommunication/, February 25, 2024.