Abstract
In today’s competitive landscape, a growing number of enterprises are embarking on cyber-physical digitalization journeys to fulfil customer expectations and vitalize manufacturing processes in a cost-effective manner. Developing on top of Industry 4.0 paradigms, advanced robotics, predictive analytics, and process flow automation benefit from the use of digital replicas known as digital twins (DT). DT is a prevailing technology in manufacturing industries and leverages real-time monitoring, simulation, and decision-aid systems to generate feasible solutions to assist production operations, which can range from predictive maintenance to strategic planning. As a trending technology, many digital twin-driven approaches are formulated to achieve mass customization and implement smart product service systems. Hence, this chapter aims to provide insights into the various digital twin architectures and development trends in manufacturing environments. It also proposes the integration of other emerging technologies to allow stakeholders who are capitalizing on this technology to gain a significant competitive edge in the present competitive environment. With consideration to both business innovation and engineering product lifecycle management, this work serves as a guide to highlight the status of digital twin development trends and benefits with innovative use cases aiming at setting a consistent standard for digital twin creation throughout academia and industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Oztemel, E., Gursev, S.: Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 31(1), 127–182 (2020). https://doi.org/10.1007/s10845-018-1433-8
Li, X., Chen, C.-H., Zheng, P., Wang, Z., Jiang, Z., Jiang, Z.: A knowledge graph-aided C-K approach for evolutionary smart product-service system development. J. Mech. Des. (May), 1–43 (2020). https://doi.org/10.1115/1.4046807
Lu, Y., Xu, X.: Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robot. Comput. Integr. Manuf. 57 (October 2018), 92–102 (2019). https://doi.org/10.1016/j.rcim.2018.11.006
Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T.: Intelligent Manufacturing in the Context of Industry 4.0: a Review. Engineering 3(5), 616–630 (2017). https://doi.org/10.1016/j.eng.2017.05.015
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018). https://doi.org/10.1016/j.ifacol.2018.08.474
Lim, K.Y.H., Zheng, P., Chen, C.: A state-of-the-art survey of digital twin: techniques, engineering product lifecycle management and business innovation perspectives. J. Intell. Manuf. (2019). https://doi.org/10.1007/s10845-019-01512-w
Cohen, Y., Naseraldin, H., Chaudhuri, A., Pilati, F.: Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0. Int. J. Adv. Manuf. Technol. 105(9), 4037–4054 (2019). https://doi.org/10.1007/s00170-019-04203-1
Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B.: Characterising the digital twin: a systematic literature review. CIRP J. Manuf. Sci. Technol. (2019) (2020). https://doi.org/10.1016/j.cirpj.2020.02.002
Maleki, E., Belkadi, F., Ritou, M., Bernard, A.: A tailored ontology supporting sensor implementation for the maintenance of industrial machines. Sensors (Switzerland) 17(9) (2017). https://doi.org/10.3390/s17092063
Cavalcante, I.M., Frazzon, E.M., Forcellini, F.A., Ivanov, D.: A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int. J. Inf. Manage. 49(February), 86–97 (2019). https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Lu, Y., Xu, X.: A semantic web-based framework for service composition in a cloud manufacturing environment. J. Manuf. Syst. 42, 69–81 (2017). https://doi.org/10.1016/j.jmsy.2016.11.004
Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. Whitepaper (2014). https://doi.org/10.5281/zenodo.1493930
Rasheed, A., San, O., Kvamsdal, T.: Digital twin: values, challenges and enablers, pp. 1–31 (2019)
Zheng, P., Xu, X., Chen, C.H.: A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment. J. Intell. Manuf., 1–16 (2018). https://doi.org/10.1007/s10845-018-1430-y
Srinivasan, S.: Guide to big data applications (2018)
Giovannini, A., Aubry, A., Panetto, H., El Haouzi, H., Canciglieri, O., Pierrel, L.: Knowledge representation, retrieval and reuse for product family design: an anti-logicist approach. Comput. Ind. Eng. 101, 391–402 (2016). https://doi.org/10.1016/j.cie.2016.10.001
Zheng, P., Sivabalan, A.S.: A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment. Robot. Comput. Integr. Manuf. 64(February), 101958 (2020). https://doi.org/10.1016/j.rcim.2020.101958
Lim, K.Y.H., Zheng, P., Chen, C., Huang, L.: A digital twin-enhanced system for engineering product family design and optimization. J. Manuf. Syst. 57(August), 82–93 (2020). https://doi.org/10.1016/j.jmsy.2020.08.011
Qi, Q. et al.: Enabling technologies and tools for digital twin. J. Manuf. Syst. (October), 0–1 (2019). https://doi.org/10.1016/j.jmsy.2019.10.001
Øvern, A.: Industry 4.0—digital twins and OPC UA (2018)
Zheng, P., Wang, Z., Chen, C.H., Pheng Khoo, L.: A survey of smart product-service systems: key aspects, challenges and future perspectives. Adv. Eng. Inf. 42(July), 100973 (2019). https://doi.org/10.1016/j.aei.2019.100973
Qi, Q., Tao, F.: digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018). https://doi.org/10.1109/access.2018.2793265
Tao, F., Qi, Q., Wang, L., Nee, A.Y.C.: Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering 5(4), 653–661 (2019). https://doi.org/10.1016/j.eng.2019.01.014
Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., Xu, X.: Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 61(April 2019), 101837 (2020). https://doi.org/10.1016/j.rcim.2019.101837
Yang, S., Wang, J., Shi, L., Tan, Y., Qiao, F.: Engineering management for high-end equipment intelligent manufacturing. Front. Eng. Manag. 5(4), 420 (2018). https://doi.org/10.15302/j-fem-2018050
Rosa, P., Sassanelli, C., Urbinati, A., Chiaroni, D., Terzi, S.: Assessing relations between Circular Economy and Industry 4.0: a systematic literature review. Int. J. Prod. Res. 58(6), 1662–1687 (2020). https://doi.org/10.1080/00207543.2019.1680896
Söderberg, R., Wärmefjord, K., Carlson, J.S., Lindkvist, L.: Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Ann. Manuf. Technol. 66(1), 137–140 (2017). https://doi.org/10.1016/j.cirp.2017.04.038
Schleich, B., Anwer, N., Mathieu, L., Wartzack, S.: Shaping the digital twin for design and production engineering. CIRP Ann. Manuf. Technol. 66(1), 141–144 (2017). https://doi.org/10.1016/j.cirp.2017.04.040
Xu, X.: Machine Tool 4.0 for the new era of manufacturing. Int. J. Adv. Manuf. Technol. 92(5–8), 1893–1900 (2017). https://doi.org/10.1007/s00170-017-0300-7
Luo, W., Hu, T., Zhang, C., Wei, Y.: Digital twin for CNC machine tool: modeling and using strategy. J. Ambient Intell. Humaniz. Comput. 10(3), 1129–1140 (2018). https://doi.org/10.1007/s12652-018-0946-5
Zhang, H., Liu, Q., Chen, X., Zhang, D., Leng, J.: A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 5, 26901–26911 (2017). https://doi.org/10.1109/access.2017.2766453
Bottani, E., Cammardella, A., Murino, T., Vespoli, S.: From the cyber-physical system to the digital twin: the process development for behaviour modelling of a cyber guided vehicle in M2M logic, pp. 96–102 (2017)
Petkovi, T., Puljiz, D., Markovic, I., Hein, B.: Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses. Robot. Comput. Integr. Manuf. 57, 182–196 (2019). https://doi.org/10.1016/j.rcim.2018.11.004
Liu, J., Zhou, H., Tian, G., Liu, X., Jing, X.: Digital twin-based process reuse and evaluation approach for smart process planning. Int. J. Adv. Manuf. Technol., 1619–1634 (2018). https://doi.org/10.1007/s00170-018-2748-5
Tao, F., Zhang, M., Liu, Y., Nee, A.Y.C.: Digital twin driven prognostics and health management for complex equipment. CIRP Ann. 67(1), 169–172 (2018). https://doi.org/10.1016/j.cirp.2018.04.055
Popa, C.L., Cotet, C.E., Popescu, D., Solea, M.F., Şaşcîm (Dumitrescu), S.G., Dobrescu, T.: Material flow design and simulation for a glass panel recycling installation. Waste Manag. Res. 36(7), 653–660 (2018). https://doi.org/10.1177/0734242x18775487
Reim, W., Parida, V., Örtqvist, D.: Product-Service Systems (PSS) business models and tactics—a systematic literature review. J. Clean. Prod. 97, 61–75 (2015). https://doi.org/10.1016/j.jclepro.2014.07.003
Ardolino, M., Rapaccini, M., Saccani, N., Gaiardelli, P., Crespi, G., Ruggeri, C.: The role of digital technologies for the service transformation of industrial companies. Int. J. Prod. Res. 56(6), 2116–2132 (2018). https://doi.org/10.1080/00207543.2017.1324224
Zheng, P., Lim, K.Y.H.: Product family design and optimization: a digital twin-enhanced approach. Procedia CIRP 93, 246–250 (2020). https://doi.org/10.1016/j.procir.2018.02.026
S. Economic Development Board: Manufacturing Transformation Insights Report 2019 (2019)
Cui, Y., Kara, S., Chan, K.C.: Manufacturing big data ecosystem: a systematic literature review. Robot. Comput. Integr. Manuf. 62(September 2019), 101861 (2020). https://doi.org/10.1016/j.rcim.2019.101861
Borth, M., Verriet, J., Muller, G.: Digital twin strategies for SoS. 2019 14th Annu. Conf. Syst. Syst. Eng., 164–169 (2019). ISBN: 978-1-7281-0457-7
Huynh, B.H., Akhtar, H., Sett, M.K.: A universal methodology to create digital twins for serial and parallel manipulators. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 3104–3109 (2019). https://doi.org/10.1109/smc.2019.8914195
Myo, K.S., Huynh, B.H., Humza, A., Wang, W.: Digital twin development for serial manipulators: data driven optimized planning and sequencing of tasks (2019)
Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58(10), 2904–2915 (2020). https://doi.org/10.1080/00207543.2020.1750727
APICS: Supply chain operations reference model (2017)
Tran, L.V., Huynh, B.H., Akhtar, H.: Ant colony optimization algorithm for maintenance, repair and overhaul scheduling optimization in the context of Industrie 4.0. Appl. Sci. 9(22) (2019). https://doi.org/10.3390/app9224815
Acknowledgements
This research is supported by the Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing & Engineering (AME) Industry Alignment Funding - Pre-positioning funding scheme (Project No: A1723a0035).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lim, K.Y.H., Le, N.T., Agarwal, N., Huynh, B.H. (2021). Digital Twin Architecture and Development Trends on Manufacturing Topologies. In: Toro, C., Wang, W., Akhtar, H. (eds) Implementing Industry 4.0. Intelligent Systems Reference Library, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-030-67270-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-67270-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67269-0
Online ISBN: 978-3-030-67270-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)