Implementation of an Energy Metering System for Smart Production

  • Friedrich A. HalstenbergEmail author
  • Kai Lindow
  • Rainer Stark


Digitization is consecutively changing more and more areas of human living. Many products are designed increasingly “smart” and connected to their environment. Not only products but also the necessary production facilities and systems are subject to digital change. The goal is to achieve a wide range of improvements and increase the efficiency and flexibility of the interlinked production systems. In Industry 4.0, important production parameters are measured and monitored with the help of sensors. Based on analyses of those data, adjustments and improvements of the production system can be performed. This paper presents the concept and physical implementation of an advanced energy metering system on a factory demonstrator, the so-called SmartFactory 4.0. It produces beverage coasters, which can be designed freely by the customer in shape, material and colour and is produced directly or remotely through a web application. The SmartFactory 4.0 consists of three production modules, which are connected to one another by means of media and information technology. The advanced energy metering system is designed in order to measure and monitor energy consumptions in various production steps. Those data are compared to previous simulations. Steps for the improvement of the energy efficiency of the SmartFactory 4.0 are derived.

This paper presents first test results from the application of the system. For different individualized gravures and two different colours (green and orange) with various depths of the produced beverage coaster, energy consumptions of the production have been metered over time. The measured data are analysed and evaluated, and suitable steps for improvement are given. Finally, this research provides suggestions for scaling the energy metering system to larger production systems, and a systematic procedure for implementation is given. This research constitutes one step in the direction of utilizing the concept of the digital factory twin for the improvement of energy efficiency and sustainability of production systems.


Digitization Digital twin Energy metering Energy efficiency Smart manufacturing Smart factory 


  1. 1.
    Thames L, Schaefer D. Cybersecurity for industry 4.0. New York: Springer; 2017.CrossRefGoogle Scholar
  2. 2.
    Ríos J, Bernard A, Bouras A et al. Product Lifecycle Management and the Industry of the Future: 14th IFIP WG 5.1 International Conference, PLM 2017, Seville, Spain, July 10–12, 2017, Revised Selected Papers. New York: Springer; 2017Google Scholar
  3. 3.
    Giusto D, Iera A, Morabito G, et al. The internet of things: 20th Tyrrhenian workshop on digital communications. New York: Springer Science & Business Media; 2010.CrossRefGoogle Scholar
  4. 4.
    Wang S, Wan J, Li D, et al. Implementing smart factory of industrie 4.0: an outlook. Int J Distributed Sensor Netw. 2016;12(1):3159805.CrossRefGoogle Scholar
  5. 5.
    McKinsey Report. How to Navigate Digitization of the Manufacturing Sector; 2015.Google Scholar
  6. 6.
    Lödding H, Riedel R, Thoben K-D, et al. Advances in production management systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing: IFIP WG 5.7 International Conference, APMS 2017Hamburg, Germany, September 3–7, 2017, Proceedings. New York: Springer; 2017.Google Scholar
  7. 7.
  8. 8.
    Davis J, Edgar T, Porter J, et al. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng. 2012;47:145–56.CrossRefGoogle Scholar
  9. 9.
    Adolphs P, Bedenbender H, Dirzus D et al. Reference architecture model industrie 4.0 (rami4. 0). ZVEI and VDI, Status Report; 2015.Google Scholar
  10. 10.
    Grieves M, Vickers J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen F-J, Flumerfelt S, Alves A, editors. Transdisciplinary perspectives on complex systems. New York: Springer; 2017. p. 85–113.CrossRefGoogle Scholar
  11. 11.
    Stark R, Kind S, Neumeyer S. Innovations in digital modelling for next generation manufacturing system design. CIRP Ann. 2017;66(1):169–72.CrossRefGoogle Scholar
  12. 12.
    Larek R, Brinksmeier E, Meyer D, et al. A discrete-event simulation approach to predict power consumption in machining processes. Prod Eng. 2011;5(5):575.CrossRefGoogle Scholar
  13. 13.
    O’Driscoll E, Cusack DO, O’Donnell GE. Implementation of energy metering systems in complex manufacturing facilities–a case study in a biomedical facility. 9th CIRP IPSS Conference: Circular Perspectives on PSS 1; 2012. p. 524–529.Google Scholar
  14. 14.
    He Y, Liu F, Wu T, et al. Analysis and estimation of energy consumption for numerical control machining. Proc Inst Mech Eng B J Eng Manuf. 2012;226(2):255–66.CrossRefGoogle Scholar
  15. 15.
    Duflou JR, Kellens K, Guo Y, et al. Critical comparison of methods to determine the energy input for discrete manufacturing processes. CIRP Ann. 2012;61(1):63–6.CrossRefGoogle Scholar
  16. 16.
    Damerau T, Vorsatz T. Batch size 1: the »Smart Factory 4.0« demonstration cell. Futur 2016;1–3.Google Scholar
  17. 17.
    Open Energy Monitor. Resources. 2017. Accessed 28 Aug 2018.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Friedrich A. Halstenberg
    • 1
    Email author
  • Kai Lindow
    • 1
  • Rainer Stark
    • 1
  1. 1.Division of Virtual Product CreationFraunhofer Institute for Production Systems and Design TechnologyBerlinGermany

Personalised recommendations