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Towards Digital Twins Development and Implementation to Support Sustainability – Systematic Literature Review

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Digital twin (DT) is a solution for presenting reality in a virtual world. DTs have been discussed in the literature only recently. The aim of this work is to review and analyse literature connected to DTs. Under a systematic literature review the authors searched databases for the information how DTs can support organization operations and how they can support sustainability of companies. A literature review was performed according to a developed research methodology, which covers research questions and keywords identification, selection criteria and results analysis. Databases, such as Web of Science, Scopus and Science Direct, were searched. The titles, abstracts and keywords were searched for works related to digital twins, sustainable development and manufacturing processes. Moreover, the search was focused on real-time monitoring, data, decision-making etc. The keywords used in the searching process are specified in the methodology. Afterwards, quantitate and qualitative analysis were performed taking into account number of publication, year of publications, type of publication, based on keywords and available information concerning the papers. Deeper analysis was performed on available full texts of the papers. The main goal of this paper was to assess how much the specified problem is discussed in literature in the context of production organizations and real-time and what kind of topics are present in publications to indicate future research needs.
Twórcy
autor
  • Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Poland
  • Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
Bibliografia
  • Aivaliotis, P., Georgoulias, K., and Chryssolouris, G. (2019). The use of Digital Twin for predictive maintenance in manufacturing. Int. J. Comput. Integr. Manuf. 32, 1067–1080, DOI: 10.1080/0951192X.2019.1686173.
  • Barni, A., Fontana, A., Menato, S., Sorlini, M., and Canetta, L. (2018). Exploiting the Digital Twin in the Assessment and Optimization of Sustainability Performances. In: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 – Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 706–713, DOI: 10.1109/IS.2018.8710554.
  • Barthelmey, A., Lee, E., Hana, R., and Deuse, J. (2019). Dynamic digital twin for predictive maintenance in flexible production systems. In: IECON Proceedings (Industrial Electronics Conference). IEEE Computer Society, pp. 4209–4214, DOI: 10.1109/IECON.2019. 8927397.
  • Bazaz, S.M., Lohtander, M., and Varis, J. (2019). 5- Dimensional Definition for a Manufacturing Digital Twin. Procedia Manuf., 38, 1705–1712, DOI: 10.1016/j.promfg.2020.01.107.
  • Biesinger, F., Meike, D., Kraß, B., and Weyrich, M. (2019). A digital twin for production planning based on cyber-physical systems: A Case Study for a CyberPhysical System-Based Creation of a Digital Twin. In: Procedia CIRP. Elsevier B.V., pp. 355–360, DOI: 10.1016/j.procir.2019.02.087.
  • Cattaneo, L. and MacChi, M. (2019). A Digital Twin Proof of Concept to Support Machine Prognostics with Low Availability of Run-To-Failure Data. IFACPapersOnLine 52, 37–42, DOI: 10.1016/j.ifacol. 2019.10.016.
  • Constantinescu, C., Giosan, S., Matei, R., and Wohlfeld, D. (2020). A holistic methodology for development of Real-Time Digital Twins. Procedia CIRP, vol. 88, pp. 163–166. DOI: 10.1016/j.procir.2020.05.029.
  • Czwick, C. and Anderl, R. (2020). Cyber-physical twins – definition, conception and benefit. Procedia CIRP 90, 584–588, DOI: 10.1016/j.procir.2020.01.070.
  • Dostatni, E., Diakun, J., Grajewski, D., Wichniarek, R., and Karwasz, A. (2015). Functionality assessment of ecodesign support system. Manag. Prod. Eng. Rev., 6, 10–15, DOI: 10.1515/mper-2015-0002.
  • Evangeline, P. and Anandhakumar P. (2020). Digital twin technology for “smart manufacturing”. Adv. Comput., 117, 35–49.
  • Ferrario, A., Confalonieri, M., Barni, A., Izzo, G., Landolfi, G., and Pedrazzoli, P. (2019). A multipurpose small-scale smart factory for educational and research activities. Procedia Manufacturing, vol. 38, pp. 663– 670. DOI: 10.1016/j.promfg.2020.01.085.
  • Gkournelos, C., Kousi, N., Bavelos, A.C., Aivaliotis, S., Giannoulis, C., Michalos, G., and Makris, S. (2019). Model based reconfiguration of flexible production systems. Procedia CIRP 86, 80–85. DOI: 10.1016/j.procir.2020.01.042.
  • Grieves, M. and Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. in: Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Springer International Publishing, pp. 85–113, DOI: 10.1007/978-3-319-38756-7_4.
  • Hamrol, A., Gawlik, J. and Sładek, J. (2019). Mechanical engineering in industry 4.0. Manag. Prod. Eng. Rev. 10, 14–28, DOI: 10.24425/mper.2019.129595.
  • He, B. and Bai, K.J. (2020). Digital twin-based sustainable intelligent manufacturing: a review. Adv. Manuf., DOI: 10.1007/s40436-020-00302-5.
  • Hungud, V., Arunachalam, S. K. (2020). Digital twin: Empowering edge devices to be intelligent. In: Advances in Computers. Editors: Pethuru Raj, Preetha Evangeline, Elsevier, Volume 117, Issue 1, pp. 107–127. DOI: 10.1016/bs.adcom.2019.10.005.
  • Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G., and Makris, S. (2019). Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf., 28, 121–126. DOI: 10.1016/j.promfg.2018.12.020.
  • Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51, 1016–1022. DOI: 10.1016/j.ifacol.2018.08.474.
  • Latif, H. and Starly, B. (2020). A Simulation Algorithm of a Digital Twin for Manual Assembly Process. Procedia Manuf., 48, 932–939. DOI: 10.1016/j.promfg.2020.05.132.
  • Lee, J., Bagheri, B., and Kao, H.A. (2015). A CyberPhysical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett., 3, 18–23, DOI: 10.1016/j.mfglet.2014.12.001.
  • Liu, X., Liu, J., Zhou, H., and Ni, Z. (2020). Digital twin driven process design evaluation. In: Digital Twin Driven Smart Design. Editors: Fei Tao, Ang Liu, Tianliang Hu, A.Y.C. Nee, Academic Press, pp. 309–332, DOI: 10.1016/B978-0-12-818918-4.00012-9.
  • Min, Q., Lu, Y., Liu, Z., Su, C., and Wang, B. (2019). Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry. Int. J. Inf. Manage., 49, 502–519, DOI: 10.1016/j.ijinfomgt.2019.05.020.
  • Nåfors, D., Berglund, J., Gong, L., Johansson, B., Sandberg, T., and Birberg, J. (2020). Application of a Hybrid Digital Twin Concept for Factory Layout. Smart and Sustainable Manufacturing Systems, 4, 2, 231–244, DOI: 10.1520/SSMS20190033.
  • Negri, E., Fumagalli, L., and Macchi, M. (2017). A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manuf., 11, 939–948, DOI: 10.1016/j.promfg.2017.07.198.
  • Rosen, R., Von Wichert, G., Lo, G., and Bettenhausen, K.D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. In: IFAC-PapersOnLine. Elsevier, pp. 567–572, DOI: 10.1016/j.ifacol.2015.06.141.
  • Schützer, K., de Andrade Bertazzi, J., Sallati, C., Anderl, R., and Zancul, E. (2019). Contribution to the development of a Digital Twin based on product lifecycle to support the manufacturing process. Procedia CIRP 84, 82–87, DOI: 10.1016/j.procir.2019.03. 212.
  • Söderberg, R., Wärmefjord, K., Carlson, J.S., and Lindkvist, L. (2017). Toward a Digital Twin for realtime geometry assurance in individualized production. CIRP Journal of Manufacturing Science and Technology, 66, 137–140. DOI: 10.1016/j.cirp.2017.04.038.
  • Stadnicka, D. and Sakano, K. (2017). Employees motivation and openness for continuous improvement: Comparative study in Polish and Japanese companies. Manag. Prod. Eng. Rev., 8, 70–86, DOI: 10.1515/mper-2017-0030.
  • Stock, T. and Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. In: Procedia CIRP. Elsevier B.V., pp. 536–541, DOI: 10.1016/j.procir.2016.01.129.
  • Sustainable Development Goals (2021). https://www.un.org/sustainabledevelopment/.
  • Taylor, C., Murphy, A., Butterfield, J., Jan, Y., Higgins, P., Collins, R., and Higgins, C. (2018). Defining Production and Financial Data Streams Required for a Factory Digital Twin to Optimise the Deployment of Labour. In: Communications in Computer and Information Science. Springer Verlag, pp. 3–12, DOI: 10.1007/978-981-13-2396-6_1.
  • Uhlemann, T.H.J., Schock, C., Lehmann, C., Freiberger, S., and Steinhilper, R. (2017). The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems. Procedia Manuf., 9, 113–120, DOI: 10.1016/j.promfg.2017.04.043.
  • Wang, J., Huang, Y., Chang, Q., and Li, S. (2019). Eventdriven online machine state decision for energyefficient manufacturing system based on digital twin using Max-plus Algebra. Sustain., 11, DOI: 10.3390/su11185036.
  • Yan, J. and Li, B. (2020). Research hotspots and tendency of intelligent manufacturing. Kexue Tongbao/Chinese Sci. Bull., 65, 684–694, DOI: 10.1360/N972019- 00125.
  • Zabala, L., Febres, J., Sterling, R., López, S., and Keane, M. (2020). Virtual testbed for model predictive control development in district cooling systems. Renew. Sustain. Energy Rev., 129, 109920, DOI: 10.1016/j.rser.2020.109920.
  • Zhang, K., Qu, T., Zhou, D., Jiang, H., Lin, Y., Li, P., Guo, H., Liu, Y., Li, C., and Huang, G.Q. (2020). Digital twin-based opti-state control method for a synchronized production operation system. Robot. Comput. Integr. Manuf., 63, 101892, DOI: 10.1016/j.rcim.2019.101892.
  • Zhuang, C., Liu, J., and Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shopfloor. Int. J. Adv. Manuf. Technol., 96, 1149–1163, DOI: 10.1007/s00170-018-1617-6.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4d515818-5429-458d-9570-349b83fe1ec2
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