Scalable Digital Twins for industry 4.0 digital services: a dataspaces approach

References

• Aheleroff, S., Mostashiri, N., Xu, X., & Zhong, R. Y. (2021). Mass personalisation as a service in industry 4.0: A resilient response case study. Advanced Engineering Informatics, 50(June), 101438. https://doi.org/10.1016/j.aei.2021.101438
   Google Scholar
• Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital twin as a service (DTaaS) in industry 4.0: An architecture reference model. Advanced Engineering Informatics, 47(August 2020), 101225. https://doi.org/10.1016/j.aei.2020.101225
   Google Scholar
• Arabnia, H. R., Deligiannidis, L., & Tinetti, F. G. (2018). Frontiers in education. Computer Science and Computer Engineering.
   Google Scholar
• Bader, S., Barnstedt, E., Bedenbender, H., Billman, M., Boss, B., & Braunmandl, A. (2020). Details of the asset administration shell part 1 - the exchange of information between partners in the value chain of industrie 4.0. Plattform Industrie, 4(0), 473.
   Google Scholar
• Bazaz, S. M., Lohtander, M., & Varis, J. (2020). Availability of manufacturing data resources in digital twin. Procedia Manufacturing, 51, 1125–34. https://doi.org/10.1016/j.promfg.2020.10.158
   Google Scholar
• Belyaev, A., Block, C., Boss, B., Diedrich, C., Juhel, P., Hartmann, W., Hillermeier, O., Ondracek, N., Pfeifer, S., Scherenschlich, F., & Schmelter, J. (2021). Modelling the semantics of data of an asset administration shell with elements of ECLASS. 54. https://www.plattform-i40.de/PI40/Redaktion/DE/Downloads/Publikation/Whitepaper_Plattform-Eclass.pdf?__blob=publicationFile&v=11
   Google Scholar
• Bhandari, P. (2020). An introduction to qualitative research. https://www.scribbr.com/methodology/qualitative-research/
   Google Scholar
• Cai, Y., Starly, B., Cohen, P., & Lee, Y. S. (2017). Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Procedia Manufacturing, 10, 1031–1042. https://doi.org/10.1016/j.promfg.2017.07.094
   Google Scholar
• Federal Ministry for Economic Affairs and Energy. (2019). Plattform Industrie 4.0 - RAMI4.0 – a reference framework for digitalisation. Plattform Industrie 4.0.
   Google Scholar
• Grieves, M. (2014). Digital twin: Manufacturing Excellence through Virtual Factory Replication - a whitepaper by Dr. Michael Grieves. White Paper, March, 1–7.
   Google Scholar
• Hermann, M., Pentek, T., & Otto, B. (2015). Design principles for industrie 4.0 scenarios: A literature review. Technische Universitat Dortmund, 1(1), 4–16. https://doi.org/10.13140/RG.2.2.29269.22248
   Google Scholar
• Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002
   Web of Science ®Google Scholar
• Huber, M., Wessel, S., Brost, G., & Menz, N. (2022). Building trust in data spaces. In B. Otto, M. ten Hompel, & S. Wrobel (Eds.), Designing data spaces. Springer. https://doi.org/10.1007/978-3-030-93975-5_9
   Google Scholar
• IDSA. (2019). IDS – A STANDARD FOR DATA SOVEREIGNTY AND AN INDISPENSABLE ELEMENT OF DATA ECOSYSTEM. International Data Spaces Association. Whitepaper.
   Google Scholar
• Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnline, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
   Google Scholar
• Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021). Digital twins-based smart manufacturing system design in industry 4.0: A review. Journal of Manufacturing Systems, 60(May), 119–137. https://doi.org/10.1016/j.jmsy.2021.05.011
   Google Scholar
• Li, G., Hou, Y., & Wu, A. (2017). Fourth industrial revolution: Technological drivers, impacts and coping methods. Chinese Geographical Science, 27(4), 626–637. https://doi.org/10.1007/s11769-017-0890-x
   Web of Science ®Google Scholar
• Lu, Y., Liu, C., Wang, K. I. K., Huang, H., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and computer-integrated manufacturing, 61, 101837. https://doi.org/10.1016/j.rcim.2019.101837
   Web of Science ®Google Scholar
• Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179. https://doi.org/10.1016/j.psep.2018.04.018
   Web of Science ®Google Scholar
• Moreno, T., Almeida, A., Ferreira, F., Caldas, N., Toscano, C., & Azevedo, A. (2021). Digital twin for manufacturing equipment in industry 4.0. Advances in Transdisciplinary Engineering, 15, 362–367. https://doi.org/10.3233/ATDE210062
   Google Scholar
• Nast, M., Rother, B., Golatowski, F., Timmermann, D., Leveling, J., Olms, C., & Nissen, C. 2020. Work-in-progress: Towards an international data spaces connector for the internet of things. IEEE Int. Work. Fact. Commun. Syst. - Proceedings, WFCS. 2020-April. https://doi.org/10.1109/WFCS47810.2020.9114503.
   Google Scholar
• Ocker, F., Urban, C., Vogel-Heuser, B., & Diedrich, C. (2021). Leveraging the asset administration shell for agent-based production systems. IFAC-PapersOnline, 54(1), 837–844. https://doi.org/10.1016/j.ifacol.2021.08.186
   Google Scholar
• Otto, B. (2022). The evolution of data spaces. In B. Otto, M. ten Hompel, & S. Wrobel (Eds.), Designing data spaces. Springer. https://doi.org/10.1007/978-3-030-93975-5_1
   Google Scholar
• Otto, B., Steinbuß, S., Teuscher, A., Lohmann, S., Auer, S., Bader, S., Bastiaansen, H., Bauer, H., Birnstil, P., Böhmer, M., Bohn, J., Böge, G., Brettner, U., Brost, G., Ceballos, G., Cirullies, J., Corsi, E., Dalmolen, S., Danielsen, S., Duisberg, A., et al. (2019). International data spaces reference architecture model version 3.0. April.
   Google Scholar
• Pettenpohl, H., Spiekermann, M., & Both, J. R. (2022). International data spaces in a nutshell. In B. Otto, M. ten Hompel, & S. Wrobel (Eds.), Designing data spaces. Springer. https://doi.org/10.1007/978-3-030-93975-5_3
   Google Scholar
• Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability (Switzerland), 10(10), 1–24. https://doi.org/10.3390/su10103821
   Web of Science ®Google Scholar
• Pribiš, R., Beňo, L., & Drahoš, P. (2021). Asset administration shell design methodology using embedded opc unified architecture server. Electronics (Switzerland), 10(20), 2520. https://doi.org/10.3390/electronics10202520
   Google Scholar
• Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 58(October 2019), 3–21. https://doi.org/10.1016/j.jmsy.2019.10.001
   Google Scholar
• Qi, Q., Tao, F., Zuo, Y., & Zhao, D. (2018). Digital twin service towards smart manufacturing. Procedia CIRP, 72(June), 237–242. https://doi.org/10.1016/j.procir.2018.03.103
   Google Scholar
• Radziwon, A., Bilberg, A., Bogers, M., & Madsen, E. S. (2014). The smart factory: Exploring adaptive and flexible manufacturing solutions. Procedia Engineering, 69, 1184–1190. https://doi.org/10.1016/j.proeng.2014.03.108
   Google Scholar
• Rajendran, S., & Pagel, E. (2020). Insights on next-generation manufacturing of smart devices using text analytics. Heliyon, 6(7), e04491. https://doi.org/10.1016/j.heliyon.2020.e04491
   PubMed Web of Science ®Google Scholar
• Redeker, M., Weskamp, J. N., Rössl, B., & Pethig, F. (2022). A digital twin platform for industrie 4.0. In E. Curry, S. Scerri, & T. Tuikka (Eds.), Data Spaces. Springer. https://doi.org/10.1007/978-3-030-98636-0_9
   Google Scholar
• Rolle, R. P., Martucci, V. D. O., & Godoy, E. P. (2019). Digitalization of manufacturing processes: Proposal and experimental results. 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2019 - Proceedings, June, 426–431. https://doi.org/10.1109/METROI4.2019.8792838
   Google Scholar
• Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnline, 48(3), 567–572. https://doi.org/10.1016/j.ifacol.2015.06.141
   Google Scholar
• Sanders, A., Elangeswaran, C., & Wulfsberg, J. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811–833. https://doi.org/10.3926/jiem.1940
   Google Scholar
• Santos, R., Basto, J., Alcalá, S. G. S., Frazzon, E., & Azevedo, A. (2019). Industrial IoTintegrated with simulation -A digital twin approach to support real-time decision making. Proceedings of the International Conference on Industrial Engineering and Operations Management, July (pp. 816–828). IEOM Society International
   Google Scholar
• Schleich, B., Anwer, N., Mathieu, L., & Wartzack, S. (2017). Shaping the digital twin for design and production engineering. CIRP Annals - Manufacturing Technology, 66(1), 141–144. https://doi.org/10.1016/j.cirp.2017.04.040
   Web of Science ®Google Scholar
• Schweichhart, K. (2019). RAMI 4.0 reference architectural model for industrie 4.0. InTech, 66(2), 15.
   Google Scholar
• Silva, H. D., Azevedo, M., & Soares, A. L. (2021). A vision for a platform-based digital-twin ecosystem. IFAC-PapersOnline, 54(1), 761–766. https://doi.org/10.1016/j.ifacol.2021.08.088
   Google Scholar
• Stojanovic, L., Usländer, T., Volz, F., Weißenbacher, C., Müller, J., Jacoby, M., & Bischoff, T. (2021). Methodology and tools for digital twin management–The FA3ST approach. IoT, 2(4), 717–740. https://doi.org/10.3390/iot2040036
   Google Scholar
• Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison. Engineering, 5(4), 653–661. https://doi.org/10.1016/j.eng.2019.01.014
   Web of Science ®Google Scholar
• Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C. Y., & Nee, A. Y. C. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
   Web of Science ®Google Scholar
• Thames, L., & Schaefer, D. (2016). Software-defined cloud manufacturing for industry 4.0. Procedia CIRP, 52(September), 12–17. https://doi.org/10.1016/j.procir.2016.07.041
   Google Scholar
• Tissir, S., Fezazi, S. E., & Cherrafi, A. (2020). Industry 4.0 impact on lean manufacturing: literature review. 2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020, 2–4. https://doi.org/10.1109/LOGISTIQUA49782.2020.9353889
   Google Scholar
• Wang, Y., Towara, T., & Anderl, R. (2017). Topological approach for mapping technologies in reference architectural model industrie 4.0 (RAMI 4.0). Lecture Notes in Engineering and Computer Science, 2, 982–990. .
   Google Scholar
• Zhang, C., Xu, W., Liu, J., Liu, Z., Zhou, Z., & Pham, D. T. (2019). A reconfigurable modeling approach for digital twin-based manufacturing system. Procedia CIRP, 83, 118–125. https://doi.org/10.1016/j.procir.2019.03.141
   Google Scholar