Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines
Source abstract
Abstract
This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory for Manufacturing Systems and Automation, University of Patras, 26504 Patras, Greece
Funding
Horizon 2020 Framework Programme
References (33)
Show source-provided references
1. Chryssolouris, G. (2006). Manufacturing Systems: Theory and Practice, Springer. [2nd ed.].
2. Performance assessment of production systems with mobile robots10.1016/j.procir.2015.12.097 ↗
3. Innovations in digital modelling for next generation manufacturing system design10.1016/j.cirp.2017.04.045 ↗
4. Makris, S. (2020). Cooperating Robots for Flexible Manufacturing, Springer.10.1007/978-3-030-51591-1 ↗
5. An outlook on future assembly systems introducing robotic mobile dual arm workers10.1016/j.procir.2018.03.130 ↗
6. A design framework for adaptive digital twins10.1016/j.cirp.2020.04.086 ↗
7. Scheduling of smart intra—Factory material supply operations using mobile robots10.1080/00207543.2018.1483587 ↗
8. Development capabilities for smart products10.1016/j.cirp.2019.05.010 ↗
9. Shaping the digital twin for design and production engineering10.1016/j.cirp.2017.04.040 ↗
10. David, J., Martikkala, A., Lobov, A., and Lanz, M. (2019, January 24–26). A Unified Ontology Namespace for Enterprise Integration—A Digital Twin Case Study, Instrumentation Engineering. Proceedings of the Instrumentation Engineering, Electronics and Telecommunications, Izhevsk, Russia.10.22213/2658-3658-2019-13-22 ↗
11. Digital twin driven prognostics and health management for complex equipment10.1016/j.cirp.2018.04.055 ↗
12. A Decision Making Framework for Human Robot Collaborative Workplace Generation10.1016/j.procir.2016.02.103 ↗
13. Transformation of robotic workcells to digital twins10.1016/j.cirp.2020.03.003 ↗
14. Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines10.1016/j.promfg.2018.12.020 ↗
15. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications10.1109/ACCESS.2019.2953499 ↗
16. Florescu, A., and Barabas, S.A. (2020). Modeling and Simulation of a Flexible Manufacturing System—A Basic Component of Industry 4.0. Appl. Sci., 10.10.3390/app10228300 ↗
17. Simulation Tool for assessing the performance of Flexible Manufacturing Systems10.1088/1757-899X/398/1/012023 ↗
18. Digital twin-based sustainable intelligent manufacturing: A review10.1007/s40436-020-00302-5 ↗
19. Framework for a digital twin in manufacturing: Scope and requirements10.1016/j.mfglet.2020.04.004 ↗
20. Resman, M., Protner, J., Simic, M., and Herakovic, N. (2021). A Five-Step Approach to Planning Data-Driven Digital Twins for Discrete Manufacturing Systems. Appl. Sci., 11.10.3390/app11083639 ↗
21. Sensors 4.0–smart sensors and measurement technology enable Industry 4.010.5194/jsss-7-359-2018 ↗
22. Digital twin in industry: State-of-the-art10.1109/TII.2018.2873186 ↗
23. A survey of sensor fusion methods in wearable robotics10.1016/j.robot.2014.08.012 ↗
24. Multisensor Data Fusion and Integration for Mobile Robots: A review
25. Visual—inertial navigation systems for aerial robotics: Sensor fusion and technology10.1109/TASE.2016.2582752 ↗
26. Digital twin driven human–robot collaborative assembly10.1016/j.cirp.2019.04.011 ↗
27. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 12–17). ROS: An open-source Robot Operating System. Proceedings of the ICRA Workshop on Open Source Software, Anchorage, AK, USA.
28. Development and operation of Digital Twins for technical systems and services10.1016/j.cirp.2019.04.024 ↗
29. Seamless human robot collaborative assembly—An automotive case study10.1016/j.mechatronics.2018.08.006 ↗
30. Design Considerations for Safe Human–Robot Collaborative Workplaces10.1016/j.procir.2015.08.014 ↗
31. Pyo, Y., Cho, H., Jung, L., and Lim, D. (2017). ROS Robot Programming, ROBOTIS.
32. (2021, May 13). GAZEBO Simulator. Available online: http://gazebosim.org/.
33. (2021, May 13). Lanner Witness Simulation. Available online: https://www.lanner.com/en-us/technology/witness-simulation-software.html.