Approach of Model Extension for Virtual Commissioning to Predict Energy Consumption of Production Systems
 
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IIOT controls and technical cybernetics, Fraunhofer IWU, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
 
 
Submission date: 2023-09-01
 
 
Final revision date: 2023-11-03
 
 
Acceptance date: 2023-11-06
 
 
Online publication date: 2023-11-09
 
 
Corresponding author
Leon Hollas   

IIOT controls and technical cybernetics, Fraunhofer IWU, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
 
 
Journal of Machine Engineering 2023;23(4):77-88
 
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ABSTRACT
Not only as a result of the current energy crisis, opportunities to save energy are a highly focused topic in production. For this reason, the article proposes an approach to evaluate the part-specific energy consumption of production systems by utilization of simulation methods. As an application example, a Comau 6-axis robot is chosen, of which a physically based model is created in the CAE software SimulationX. This model is then exported as a Functional Mock-Up Unit (FMU) and co-simulated within a virtual commissioning environment. Virtual commissioning enables a controller to be connected to a model. Within a Software-in-the-Loop simulation, this is a virtual control system. Based on the movement specifications from the virtual controller, the movement behavior of the machine can be simulated in the virtual commissioning tool ISG-virtuos and the FMU returns the associated power and energy curve as a result variable. For further use, this kind of enhanced simulation models provides the possibility to optimize the utilization of production systems for specific processes in the context of a complete production line or factory.
REFERENCES (17)
1.
GANGL K., et al., 2022, Policy Brief: Energiekrise – was tun?, Institut für Höhere Studien, Vienna.
 
2.
BDEW, 2021, Verteilung des Stromverbrauchs in Deutschland nach Verbrauchergruppen, im Statista, 2022, [Online], Available: https://de.statista.com/statis....
 
3.
Statistisches Bundesamt, 2023, Energieintensivste Produktionsbereiche nach Energieverbrauch in Deutschalnd im Jahresvergleich 2010 und 2020, [Online], Available: https://de.statista.com/statis... 164011/ umfrage/ energieintensivste-und-energieaermste-industrien-deutschland-2008/.
 
4.
GOLLEE C., SELCH M., SCHENKE C.-C., HELLMICH A., IHLENFELDT S., 2022, Learning Robots for the Machining of Easily Machinable Materials Situationally Optimal Robot Models, (in ger), AT-AUTOM, 70/6, https://doi.org/10.1515/auto-2....
 
5.
FRITZSCHE R., RICHTER A., PUTZ M., 2018, Product Flexible Car Body Fixtures with Methods of Artificial Intelligence, Procedia CIRP, 67, https://doi.org/10.1016/j.proc....
 
6.
IHLENFELDT S., et al., 2021, Increasing Resilience of Production Systems by Integrated Design, Applied Sciences, 11/18, 8457, https://doi.org/10.3390/app111....
 
7.
TAO F., XIAO B., QI Q., CHENG J., JI P., 2022, Digital Twin Modeling, Journal of Maufacturing Systems, 64, 372–389, https://doi.org/10.1016/j.jmsy....
 
8.
KRYSTEK J., ALSZER S., BYSKO S., 2019, Virtual Commissioning as the Main Core of Industry 4.0 – Case Study in the Automotive Paint Shop, Advances in Intelligent Systems and Computing, Intelligent Systems in Production Engineering and Maintenance, A. Burduk, E. Chlebus, T. Nowakowski, and A. Tubis, Eds., Cham: Springer International Publishing, 370–379.
 
9.
METZNER M., et al., 2020, A System for Human-in-the-Loop Simulation of Industrial Collaborative Robot Applications, IEEE 16th International Conference on Automation Science and Engineering (CASE), https://doi.org/10.1109/CASE48....
 
10.
WÜNSCH G., 2007, Methoden für die Virtuellen Inbetriebnahme Automatisierter Produktionssysteme, TU München, ISBN 978-3-8316-0795-2.
 
11.
AIVALIOTIS P., ARKOULI Z., GEORGOULIAS K., MAKRIS S., 2021, Degradation Curves Integration in Physics-Based Models: Towards the Predictive Maintenance of Industrial Robots, Robotics and Computer-Integrated Manufacturing, 71, 102177, https://doi.org/10.1016/j.rcim....
 
12.
RITTO T.G., ROCHINHA F.A., 2021, Digital Twin, Physics-Based Model, and Machine Learning Applied to Damage Detection in Structures, Mechanical Systems and Signal Processing, 155, 107614, https://doi.org/ 10.1016/j.ymssp.2021.107614.
 
13.
AIVALIOTIS P., KALIAKATSOS-GEORGOPOULOS D., MAKRIS S., 2023, Physics-Based Modelling of Robot’s Gearbox Including Non-Linear Phenomena, International Journal of Computer Integrated Manufacturing, 1–12, https://doi.org/10.1080/095119....
 
14.
KAIGOM E.G., ROßMANN J., 2017, Physics-Based Simulation for Manual Robot Guidance—an eRobotics Approach, Robotics and Computer-Integrated Manufacturing, 43, 155–163, https://doi.org/10.1016/j.rcim. 2015.09.015.
 
15.
ZHAO S.J., SHAN J.Y., BI L.Y., 2012, 6-axis Serial Robot Simulation Based on Simulationx, AMM, 152–154, 1010–1017, https://doi.org/10.4028/www.sc....
 
16.
BI L., LIU L., 2011, 4-Axis Robot Design and Simulation Based on SimulationX, First International Conference on Robot, Vision and Signal Processing, Kaohsiung City, Taiwan, Nov. - Nov. 2011, 65–68.
 
17.
Modelica Association Project FMI, Functional Mock-up Interface 2.0.3.
 
 
CITATIONS (1):
1.
Modular Digital Twin – an approach for generating and exploiting product sustainability information towards service-oriented business models
Andreas Werner, Frauke Schuseil, Moritz Hämmerle, Sascha Schaper, Katharina Hölzle
International Journal of Production Research
 
eISSN:2391-8071
ISSN:1895-7595
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