Abstract
Many initiatives aim at describing the objectives and functionalities of the so-called digital twin of manufacturing systems. Considering the assets, the twin is meant to be able to both exhibit the actual and current states of the resources, and provide some estimates about the future behaviour of these resources. To target the sustainability pillar of future industrial systems, the energy monitoring and management are critical issues. Consequently, the integration of multi-physics models helping to model the resources inside the twin is a major issue to deal with. This article introduces a framework integrating the models inside the twin of the ARTI architecture, proposes a methodology to implement the twin on a resource and illustrates these ideas in a case study on injection moulding machines.
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
Stock, T., Seliger, G.: Opportunities of sustainable manufacturing in industry 4.0. Procedia CIRP 40, 536–541 (2016)
Giret, A., Trentesaux, D., Prabhu, V.: Sustainability in manufacturing operations scheduling: a state of the art review. J. Manuf. Syst. 37, 126–140 (2015)
Wang, H.-K., Haynes, R., Huang, H.-Z., Dong, L., Atluri, S.N.: The use of high-performance fatigue mechanics and the extended Kalman/particle filters, for diagnostics and prognostics of aircraft structures. CMES Comput. Model Eng. Sci. 105, 1–24 (2015)
Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., Wang, L. Modeling, simulation, information technology and processing roadmap. National Aeronautics and Space Administration (2012)
Valckenaers, P.: ARTI reference architecture–PROSA revisited. In: International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 1–19 (2018)
Lee, J., Bagheri, B., Kao, H.-A.: A Cyber-Physical Systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)
Alam, K.M., Saddik, A.E.: C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062 (2017)
Kouki, M., Cardin, O., Castagna, P., Cornardeau, C.: Input data management for energy related discrete event simulation modelling. J. Clean. Prod. 141, 194–207 (2017)
Francis, L.F.: Chapter 3 - melt processes. In: Francis, L.F. (ed.) Materials Processing, pp. 105–249. Academic Press, Boston (2016)
Dietmair, A., Verl, A.: A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing. Int. J. Sustain. Eng. 2, 123–133 (2009)
Schmidt, C., Li, W., Thiede, S., Kara, S., Herrmann, C.: A methodology for customized prediction of energy consumption in manufacturing industries. Int. J. Precis. Eng. Manuf.-Green Tech. 2, 163–172 (2015)
Dietmair, A., Verl, A.: Energy consumption forecasting and optimisation for tool machines. Energy 62, 63 (2009)
Kouki, M., Castagna, P., Cardin, O., Cornardeau, C.: An energy-related discrete event simulation approach. In: CIGI (2015)
Weinert, N., Chiotellis, S., Seliger, G.: Methodology for planning and operating energy-efficient production systems. CIRP Ann. Manuf. Technol. 60, 41–44 (2011)
Peng, T., Xu, X., Wang, L.: A novel energy demand modelling approach for CNC machining based on function blocks. J. Manuf. Syst. 33, 196–208 (2014)
Abele, E., Braun, S., Schraml, P.: Holistic simulation environment for energy consumption prediction of machine tools. Procedia CIRP 29, 251–256 (2015)
Herrmann, C., Thiede, S.: Process chain simulation to foster energy efficiency in manufacturing. CIRP J. Manuf. Sci. Technol. 1, 221–229 (2009)
Solding, P., Thollander, P.: Increased energy efficiency in a Swedish iron foundry through use of discrete event simulation. In: Proceedings of the 38th Conference on Winter Simulation, WSC 2006, pp 1971–1976. IEEE (2006)
Kohl, J., Spreng, S., Franke, J.: Discrete event simulation of individual energy consumption for product-varieties. Procedia CIRP 17, 517–522 (2014)
Kruse, A., Uhlemann, T.H.-J., Steinhilper, R.: Simulation-based assessment and optimization of the energy consumption in multi variant production. In: Proceedings of the 13th Global Conference on Sustainable Manufacturing - Decoupling Growth Resource Use, p. 6 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cardin, O. et al. (2020). Energy-Aware Resources in Digital Twin: The Case of Injection Moulding Machines. In: Borangiu, T., Trentesaux, D., Leitão, P., Giret Boggino, A., Botti, V. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2019. Studies in Computational Intelligence, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-030-27477-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-27477-1_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27476-4
Online ISBN: 978-3-030-27477-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)