Analysis dimensions for uniderstanding digital twins: a discussion in the Brasilian scenario
Palavras-chave:
Digital Twin; Brazilian Industry; Industry 5.0.Resumo
Resumo:
Objetivo: Identificar aspectos importantes para a análise dos gêmeos digitais e discutir os gêmeos digitais criados para a indústria brasileira com base nas características desta tecnologia.
Metodologia: Uma revisão de literatura das definições de gêmeos digitais e análise de gêmeos digitais criados para a indústria brasileira considerando estas cinco dimensões de análise.
Originalidade/relevância: Proposição e uso de um conjunto de dimensões para analisar gêmeos digitais, que pode ser usado por pesquisadores e profissionais para melhor entendimento e caracterização das propostas de gêmeos digitais.
Resultados: Considerando os gêmeos digitais criados para a indústria brasileira, descobriu-se que essas propostas se distinguem em alguns aspectos, principalmente no fluxo de dados entre objetos físicos e digitais, nível do sistema e capacidades cognitivas; mas aspectos como interoperabilidade, processos cognitivos e ciclo de vida não estão profundamente contemplados nesses gêmeos digitais. Tais aspectos, no entanto, são responsáveis pela inovação e disrupção que os gêmeos digitais podem proporcionar à indústria.
Contribuição: Definição de dimensões para análise de gêmeos digitais e comprovação da presença e ausência de características em gêmeos digitais criados para indústria brasileira.
Palavras-chave: Gêmeo Digital; Indústria Brasileira; Indústria 5.0.
Referências
Abburu, S., Berre, A.J., Jacoby, M., Roman, D., Stojanovic, L., Stojanovic, N. (2020) Cognitwin - hybrid and cognitive digital twins for the process industry. In: Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020.
Adl, A.E. (2016). The cognitive digital twins: Vision, architecture framework and categories.
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, 101225.
Al Faruque, M.A., Muthirayan, D., Yu, S.Y., Khargonekar, P.P. (2021). Cognitive digital twin for manufacturing systems. In: Proceedings of the Design, Automation and Test in Europe, DATE, vol. 2021-February, p. 440 – 445.
Ali, M.I., Patel, P., Breslin, J.G., Harik, R., Sheth, A. (2021). Cognitive digital twins for smart manufacturing. IEEE Intelligent Systems 36(2), 96–100.
Alves, R.G., Maia, R.F., Lima, F. (2023). Development of a digital twin for smart farming: Irrigation management system for water saving. Journal of Cleaner Production p. 135920.
Araujo Jr, C.A.A.d., Villanueva, J.M.M., Almeida, R.J.S.d., de Medeiros, I.E.A. (2021). Digital twins of the water cooling system in a power plant based on fuzzy logic. Sensors 21(20), 6737.
Ariesen-Verschuur, N., Verdouw, C., Tekinerdogan, B. (2022). Digital twins in greenhouse horticulture: A review. Computers and Electronics in Agriculture.
Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-820.
Beetz, J. (2021). Semantic digital twins for the built environment-a key facilitator for the European green deal? In: CEUR Workshop Proceedings, vol. 2887.
Belyaev, A. (2021). Modelling the semantics of data of an asset administration shell with elements of eclass. Joint white paper from Plattform Industrie 4.0 and ECLASS.
Boje, C., Kubicki, S., Guerriero, A. (2021). A 4d bim system architecture for the semantic web. Lecture Notes in Civil Engineering 98, 561 – 573.
Boschert, S., Heinrich, C., Rosen, R. (2018). Next generation digital twin. In: Proc. tmce, vol. 2018, pp. 7–11, Las Palmas de Gran Canaria, Spain.
Buchheit, M., Karmarkar, A., Schrecker, S., LeBlanc, J. (2017). The industrial internet of things volume g1: Reference architecture. IIC: PUB G 1.
D’Amico, R.D., Erkoyuncu, J.A., Addepalli, S., Penver, S. (2022). Cognitive digital twin: An approach to improve maintenance management. CIRP Journal of Manufacturing Science and Technology 38, 613 – 630.
Diéz, A., De Lara, J. (2021). Semantic digital twins for organizational development. In: CEUR Workshop Proceedings, vol. 2887.
Douglas, D., Kelly, G., Kassem, M. (2021). Bim, digital twin and cyber-physical systems: crossing and blurring boundaries. arXiv preprint arXiv:2106.11030.
Durão, L.F.C.S., Haag, S., Anderl, R., Schützer, K., Zancul, E. (2018). Digital twin requirements in the context of industry 4.0. In: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (eds.) Product Lifecycle Management to Support Industry 4.0, pp.204–214, Springer International Publishing, Cham, ISBN 978-3-03001614-2
Eirinakis, P., Kalaboukas, K., Lounis, S., Mourtos, I., Rozanec, J.M., Stojanovic, N., Zois, G. (2020). Enhancing cognition for digital twins. In: Proceedings - 2020 IEEE International Conference on Engineering, Technology and Innovation.
Eirinakis, P., Lounis, S., Plitsos, S., Arampatzis, G., Kalaboukas, K., Kenda, K., Lu, J., Rozanec, J.M., Stojanovic, N. (2022). Cognitive digital twins for resilience in production: A conceptual framework. Information (Switzerland) 13(1).
Falekas, G., Karlis, A. (2021). Digital twin in electrical machine control and predictive maintenance: state-of-the-art and future prospects. Energies 14(18).
Fernandes, S.V., João, D.V., Cardoso, B.B., Martins, M.A., Carvalho, E.G. (2022). Digital twin concept developing on an electrical distribution system—an application case. Energies 15(8), 2836.
Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication (2014). White paper 1(2014), 1–7.
Holler, M., Uebernickel, F., Brenner, W. (2016). Digital twin concepts in manufacturing industries-a literature review and avenues for further research. In: Proceedings of the 18th International Conference on Industrial Engineering (IJIE), Seoul, Korea, pp. 10–12.
Hyre, A., Harris, G., Osho, J., Pantelidakis, M., Mykoniatis, K., Liu, J. (2022). Digital twins: Representation, replication, reality, and relational (4rs). Manufacturing Letters 31, 20–23 (2022), ISSN 2213-8463.
João, D.V., Lodetti, P.Z., Martins, M.A.I., Almeida, J.F. (2020). Virtual and augmented reality applied in power electric utilities for human interface improvement– a study case for best practices. In: 2020 IEEE Technology & Engineering Management Conference (TEMSCON), pp. 1–4, IEEE.
Johnson-Laird, P.N.: Mental models and human reasoning (2010). Proceedings of the National Academy of Sciences 107(43), 18243–18250.
Kmetz, J. L. (2018). The Information Processing Theory of Organization: Managing technology accession in complex systems. Routledge.
Koulamas, C., Kalogeras, A. (2018). Cyber-physical systems and digital twins in the industrial internet of things [cyber-physical systems]. Computer 51(11), 95–98 (2018), https://doi.org/10.1109/MC.2018.2876181
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.
Kümpel, M., Mueller, C.A., Beetz, M. (2021). Semantic digital twins for retail logistics. In: Dynamics in Logistics: Twenty-Five Years of Interdisciplinary Logistics Research in Bremen, Germany, pp. 129–153, Springer International Publishing Cham.
Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems 65, 279–295, ISSN 0278-6125.
Lima, G., Factori, L., Junqueira, M., dos Santos, R., Borba, L., Gonçalves, O. (2022). Aplicação de conceitos de gêmeo digital e bim: Estudo de caso na gestão de pontes e viadutos. In: 4º Congresso Português de Building Information Modelling, vol. 1.
Lu, J., Zheng, X., Gharaei, A., Kalaboukas, K., Kiritsis, D. (2020). Cognitive twins for supporting decision-makings of internet of things systems. In: Proceedings of 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing: AMP 2020, pp. 105–115, Springer.
Lu, Y., Min, Q., Liu, Z., Wang, Y. (2019). An iot-enabled simulation approach for process planning and analysis: a case from the engine re-manufacturing industry. International Journal of Computer Integrated Manufacturing 32(4-5), 413– 429.
Maddikunta, P.K.R., Pham, Q.V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R., Liyanage, M. (2022). Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration 26, 100257.
McDermott, K.B., Roediger, H.L. (2018). Memory (encoding, storage, retrieval). General Psychology FA2018. Noba Project: Milwaukie, OR pp. 117–153.
Moreno, T., Almeida, A., Toscano, C., Ferreira, F., Azevedo, A. (2023). Scalable digital twins for industry 4.0 digital services: a dataspaces approach. Production & Manufacturing Research 11(1), 2173680 (2023), https://doi.org/10.1080/21693277.2023.2173680, URL https://doi.org/10.1080/21693277.2023.2173680
Müller, J.: Enabling technologies for industry 5.0 (2020). European Commission pp. 8–10.
Rebentisch, E., Rhodes, D.H., Soares, A.L., Zimmerman, R., Tavares, S. (2021). The digital twin as an enabler of digital transformation: a sociotechnical perspective. In: International Conference on Industrial Informatics (INDIN), pp. 1–6.
Schweichhart, K. (2019). Rami 4.0 reference architectural model for industrie 4.0. In-Tech 66(2), 15.
Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., Wang, L. (2012). Modeling, simulation, information technology & processing roadmap. National Aeronautics and Space Administration 32(2012), 1–38.
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94, 3563–3576.
Turnitsa, C. (2005). Extending the levels of conceptual interoperability models. In: Proceedings IEEE summer computer simulation conference, IEEE CS Press.
Verdouw, C., Tekinerdogan, B., Beulens, A., Wolfert, S. (2021). Digital twins in farming systems. Agric. Syst 189, 103046.
Wang, W., Tolk, A., Wang, W. (2009). The levels of conceptual interoperability model: applying systems engineering principles to m&s. In: Proceedings of the 2009 Spring Simulation Multiconference, pp. 1–9.
Wang, Y., Kang, X., Chen, Z. (2022). A survey of digital twin techniques in smart manufacturing and management of energy applications. Green Energy and Intelligent Transportation, p. 100014.
Wassermann, E., Fay, A. (2017). Interoperability rules for heterogeneous multi-agent systems: Levels of conceptual interoperability model applied for multi-agent systems. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 89–95, IEEE.
Wright, L., Davidson, S. (2020). How to tell the difference between a model and a digital twin. Advanced Modeling and Simulation in Engineering Sciences 7(1), 1–13.
Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L. (2021). Industry 4.0 and industry 5.0 - inception, conception and perception. Journal of Manufacturing Systems 61, 530–535.
Yu, W., Patros, P., Young, B., Klinac, E., Walmsley, T.G. (2022). Energy digital twin technology for industrial energy management: Classification, challenges and future. Renewable and Sustainable Energy Reviews 161, 112407.
Zezulka, F., Marcon, P., Vesely, I., Sajdl, O. (2016). Industry 4.0 - an introduction to the phenomenon. IFAC-PapersOnLine 49(25), 8–12.
Zheng, X., Lu, J., Kiritsis, D. (2022). The emergence of cognitive digital twin: vision, challenges and opportunities. International Journal of Production Research 60(24), 7610 – 7632.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2024 Revista Gestão & Tecnologia
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.
Os direitos, inclusive os de tradução, são reservados. É permitido citar parte de artigos sem autorização prévia desde que seja identificada a fonte. A reprodução total de artigos é proibida. Em caso de dúvidas, consulte o Editor.
