journal article·2022-09-30Open access

Conceiving a Digital Twin for a Flexible Manufacturing System

Laurence C. Magalhães, Luciano C. Magalhães, Jhonatan B. Ramos, Luciano R. Moura, Renato E. N. de Moraes, João B. Gonçalves, Wilian H. Hisatugu, Marcelo T. Souza, Luis N. L. de Lacalle, João C. E. Ferreira

Applied Sciences· Volume 12· Issue 19

Abstract

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Transformation in Industry · Flexible and Reconfigurable Manufacturing Systems · Additive Manufacturing and 3D Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Industrial Technology, Federal University of Espírito Santo, Av. Fernando Ferrari, 514—Campus de Goiabeiras, Vitória 29075-910, Brazil

Instituto SENAI de Inovação em Sistemas Embarcados, Av. Luiz Boiteux Piazza, 574, Cachoeira do Bom Jesus, Florianópolis 88032-005, Brazil

Department of Exact and Technological Sciences, Santa Cruz State University, Jorge Amado Highway, Km 16, Ilhéus 45662-900, Brazil

Department of Mechanical Engineering, The Aeronautics Advanced Manufacturing Center (CFAA), University of the Basque Country, Zamudio Tecnologic Park, 48170 Bilbao, Spain

Department of Mechanical Engineering, Federal University of Santa Catarina, Grupo de Integração da Manufatura (GRIMA), Caixa Postal 476, Florianópolis 88040-900, Brazil

Funding

Elkartek 2022 project LANVERSO · Basque government university group

References (60)

Show source-provided references
1. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop10.1007/s12652-018-0881-5 ↗
2. Human-Cyber-Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing10.1016/j.eng.2019.07.015 ↗
3. A digital-twin visualized architecture for Flexible Manufacturing System10.1016/j.jmsy.2021.05.010 ↗
4. 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 ↗
5. Performance Analysis of An Experimental Micro Flexible Manufacturing System (FMS)10.17667/riim.2016.1-2/13. ↗
6. Petrillo, A., Cioffi, R., and Felice, F.D. (2018). Fourth Industrial Revolution: Current Practices, Challenges, and Opportunities. Digital Transformation in Smart Manufacturing, IntechOpen.10.5772/intechopen.72304 ↗
7. Past, present, and future research of digital twin for smart manufacturing
8. Automation of input data to discrete event simulation for manufacturing: A review10.1142/S1793962316300016 ↗
9. A survey of recent advances in discrete input parameter discrete-event simulation optimization10.1080/07408170490438726 ↗
10. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications10.1109/ACCESS.2019.2953499 ↗
11. Digital twin in smart manufacturing
12. Digital twin-driven product design, manufacturing and service with big data10.1007/s00170-017-0233-1 ↗
13. Research, G.V. (2022, April 10). Digital Twin Market Size, Share & Trends Analysis Report by End Use (Manufacturing, Agriculture), By Solution (Component, Process, System), By Region (North America, APAC), And Segment Forecasts, 2022–2030. Available online: https://www.grandviewresearch.com/industry-analysis/digital-twin-market.
14. Hehenberger, P., and Bradley, D. (2016). Digital Twin-The Simulation Aspect. Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers, Springer.10.1007/978-3-319-32156-1 ↗
15. Glaessgen, E., and Stargel, D. (2012, January 23–26). The digital twin paradigm for future NASA and US Air Force vehicles. Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Honolulu, Hawaii.10.2514/6.2012-1818 ↗
16. Tao, F., Zhang, M., and Nee, A.Y.C. (2019). Background and concept of Digital Twin. Digital Twin Driven Smart Manufacturing, Academic Press.10.1016/B978-0-12-817630-6.00001-1 ↗
17. Yang, C., Tu, X., Autiosalo, J., Ala-Laurinaho, R., Mattila, J., Salminen, P., and Tammi, K. (2022). Extended Reality Application Framework for a Digital-Twin-Based Smart Crane. Appl. Sci., 12.10.3390/app12126030 ↗
18. Modeling, simulation, information technology & processing roadmap
19. Reengineering aircraft structural life prediction using a digital twin10.1155/2011/154798 ↗
20. Reifsnider, K., and Majumdar, P. (2013, January 8–11). Multiphysics stimulated simulation digital twin methods for fleet management. Proceedings of the 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Boston, MA, USA.10.2514/6.2013-1578 ↗
21. Building a digital twin for additive manufacturing through the exploitation of blockchain: A case analysis of the aircraft industry10.1016/j.compind.2019.04.011 ↗
22. Recent advances and trends in predictive manufacturing systems in big data environment10.1016/j.mfglet.2013.09.005 ↗
23. Digital twin: Manufacturing excellence through virtual factory replication
24. Shao, G., Jain, S., Laroque, C., Lee, L.H., Lendermann, P., and Rose, O. (2019, January 8–11). Digital twin for smart manufacturing: The simulation aspect. Proceedings of the 2019 Winter Simulation Conference (WSC), National Harbor, MD, USA.10.1109/WSC40007.2019.9004659 ↗
25. Panetta, K. (Gartner, 2017). Top 10 strategic technology trends for 2018: Digital twins, Gartner.
26. Cyber-physical systems in manufacturing10.1016/j.cirp.2016.06.005 ↗
27. Intelligent manufacturing in the context of Industry 4.0: A review10.1016/J.ENG.2017.05.015 ↗
28. A review of the roles of digital twin in CPS-based production systems10.1016/j.promfg.2017.07.198 ↗
29. Digital Twin in manufacturing: A categorical literature review and classification
30. Malakuti, S., Schlake, J., Ganz, C., Harper, K., and Petersen, H. (2019, January 22–24). Digital twin: An enabler for new business models. Proceedings of the Proceedings of the Automation Congress, Hangzhou, China.10.51202/9783181023518-807 ↗
31. Framework for a digital twin in manufacturing: Scope and requirements10.1016/j.mfglet.2020.04.004 ↗
32. Siller, H.R., Romero, D., Rabelo, R.J., and Vazquez, E. (2018, January 25–27). Advanced CPS service oriented architecture for smart injection 633 molding and molds 4.0. Proceedings of the 2018 International Conference on Intelligent Systems (IS), Funchal, Portugal.10.1109/IS.2018.8710575 ↗
33. Digital twin architecture and standards
34. Review of digital twin applications in manufacturing10.1016/j.compind.2019.103130 ↗
35. Development and operation of Digital Twins for technical systems and services10.1016/j.cirp.2019.04.024 ↗
36. Digital Twin of a Cutting Tool10.1016/j.procir.2018.03.178 ↗
37. Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing10.1016/j.promfg.2017.07.094 ↗
38. Micro Manufacturing Unit and the Corresponding 3D-Model for the Digital Twin10.1016/j.promfg.2018.06.057 ↗
39. Towards digital twins through object-oriented modelling: A machine tool case study
40. Duan, J.G., Ma, T.Y., Zhang, Q.L., Liu, Z., and Qin, J.Y. (2021). Design and application of digital twin system for the blade-rotor test rig. J. Intell. Manuf., 1–17.10.1007/s10845-021-01824-w ↗
41. Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: A case study in five-axis machining10.1007/s10845-020-01698-4 ↗
42. Real-time machining data application and service based on IMT digital twin10.1007/s10845-019-01500-0 ↗
43. Digital Twin for Machining Tool Condition Prediction10.1016/j.procir.2019.04.049 ↗
44. Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control10.1007/s00170-021-07867-w ↗
45. Digital twin for cutting tool: Modeling, application and service strategy10.1016/j.jmsy.2020.08.007 ↗
46. Zhuang, K., Shi, Z., Sun, Y., Gao, Z., and Wang, L. (2021). Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process. Symmetry, 13.10.3390/sym13081438 ↗
47. Creation of a micro cutting machine tool digital-twin using a cloud-based model-based PLM Platform: First results10.1016/j.promfg.2019.07.039 ↗
48. A six-layer architecture for the digital twin: A manufacturing case study implementation10.1007/s10845-019-01516-6 ↗
49. A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool10.1016/j.rcim.2021.102230 ↗
50. Artificial Intelligence-Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin10.1002/aisy.202100228 ↗
51. Hassani, H., Huang, X., and MacFeely, S. (2022). Impactful Digital Twin in the Healthcare Revolution. Big Data Cogn. Comput., 6.10.3390/bdcc6030083 ↗
52. The Social Shaping of the Metaverse as an Alternative to the Imaginaries of Data-Driven Smart Cities: A Study in Science, Technology, and Society10.3390/smartcities5030043 ↗
53. Hertz, L. (2022, April 10). Digital Twins and Metaverve. Available online: https://www.leewayhertz.com/digital-twin-and-metaverse/.
54. Rupareliya, K. (2022, April 10). How Does the Digital Twin and Metaverse Technologies Relate?. Available online: https://www.businessofapps.com/insights/how-does-the-digital-twin-and-metaverse-technologies-relate/.
55. Cohen, P. (2022, April 10). Is the Industrial Metaverse an Extension of Digital Twin Technology?. Available online: https://www.rcrwireless.com/20220812/metaverse/is-the-industrial-metaverse-an-extension-of-digital-twin-technology.
56. Ezell, S.J., Atkinson, R.D., Kim, I., and Cho, J. (2022, April 10). Manufacturing Digitalization: Extent of Adoption and Recommendations for Increasing Penetration in Korea and the US. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3264125.
57. Aho, E. (2022, April 10). Digital Twins and the Metaverse are Converging: Welcome to the Metafactory. Available online: https://corporate-blog.global.fujitsu.com/fgb/2022-07-14/01/.
58. Rabelo, R.J., Magalhães, L.C., and Cabral, F.G. (2020, January 23–26). Uma Proposta de Arquitetura de Referência de Gêmeo Digital para Sistemas Ciberfísicos em um cenário de Indústria 4.0. Proceedings of the Congresso Brasileiro de Automática-CBA, Virtual Conference.
59. Scholten, B. (2007). The Road to Integration: A Guide to Applying the ISA-95 Standard in Manufacturing, ISA.
60. Access control for cyber-physical systems interconnected to the cloud10.1016/j.comnet.2018.01.037 ↗