Abstract
The rapid development of robotic technologies in recent years has made industrial robots (IRs) increasingly used in various manufacturing process, and the energy problem of IRs has been paid great attention because of their massive adoption and intensive energy consumption. Energy saving of IRs therefore is in great demand for environment protection and enterprise cost reduction, and the energy modeling of IRs is the basis to achieve such goal. In this paper, a novel energy modeling method of IRs based on digital twin is proposed and the system framework is established, which mainly includes the physics-based energy model of the physical IRs, the 3D virtual robot model used to visualize and simulate on the energy consumption of IRs, the digital twin data and the ontology-based unified digitized description model for mapping the virtual model to the corresponding physical energy model. Furthermore, the mapping relation between the physical data and ontology attributes is established to enable the interaction between the physical space and the cyber space of the whole system. Finally, the feasibility and effectiveness of the proposed method is validated by a case study, and the results show that the digital twin-based energy modeling method can be efficiently used for the simulation and prediction of the energy consumption of IRs.
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References
Bi, Z.: Revisiting system paradigms from the viewpoint of manufacturing sustainability. Sustainability 3(9), 1323–1340 (2011)
Jovane, F., Yoshikawa, H., Alting, L., et al.: The incoming global technological and industrial revolution towards competitive sustainable manufacturing. CIRP Ann. 57(2), 641–659 (2008)
Paryanto, Brossog, M., Bornschlegl, M., et al.: Reducing the energy consumption of industrial robots in manufacturing systems. Int. J. Adv. Manuf. Technol. 78(5–8), 1315–1328 (2015)
Tavares, P., Silva, J.A., Costa, P., Veiga, G., Moreira, A.P.: Flexible work cell simulator using digital twin methodology for highly complex systems in industry 4.0. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds.) ROBOT 2017. AISC, vol. 693, pp. 541–552. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70833-1_44
Yang, A., Pu, J., Wong, C.B., et al.: By-pass valve control to improve energy efficiency of pneumatic drive system. Control Eng. Pract. 17(6), 623–628 (2009)
Riazi, S., Bengtsson, K., Wigstrom, O., et al.: Energy optimization of multi-robot systems. In: 11th IEEE International Conference on Automation Science and Engineering, pp. 1345–1350. IEEE, Gothenburg (2015)
Paes, K., Dewulf, W., Elst, K.V., et al.: Energy efficient trajectories for an industrial ABB robot. Procedia CIRP 15, 105–110 (2014)
Hansen, C., Oltjen, J., Meike, D., et al.: Enhanced approach for energy-efficient trajectory generation of industrial robots. In: IEEE International Conference on Automation Science and Engineering, pp. 1–7. IEEE, Seoul (2012)
Diken, H.: Energy efficient sinusoidal path planning of robot manipulators. Mech. Mach. Theory 29(6), 785–792 (1994)
Rassolkin, A., Hoimoja, H., Teemets, R.: Energy saving possibilities in the industrial robot IRB 1600 control. In: 7th International Conference-Workshop Compatibility and Power Electronics, pp. 226–229. IEEE, Tallinn (2011)
Smetanova, A.: Optimization of energy by robot movement. Modern Mach. Sci. J. 3(1), 172–176 (2010)
Chemnitz, M., Schreck, G., Krüger, J.: Analyzing energy consumption of industrial robots. In: IEEE 16th Conference on Emerging Technologies and Factory Automation, pp. 1–4. IEEE, Toulouse (2011)
Bjorkenstam, S., Gleeson, D., Bohlin, R., et al.: Energy efficient and collision free motion of industrial robots using optimal control. In: IEEE International Conference on Automation Science and Engineering, pp. 510–515. IEEE, Madison (2013)
Paryanto, Brossog, M., Kohl, J., et al.: Energy consumption and dynamic behavior analysis of a six-axis industrial robot in an assembly system. Procedia CIRP 23, 131–136 (2014)
Othman, A., Belda, K., Burget, P.: Physical modelling of energy consumption of industrial articulated robots. In: 15th International Conference on Control, Automation and Systems, pp. 784–789. IEEE, Busan (2015)
Liu, A., Liu, H., Yao, B., et al.: Energy consumption modeling of industrial robot based on simulated power data and parameter identification. Adv. Mech. Eng. 10(5), 1–11 (2018)
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-38756-7_4
Tuegel, E.J., Ingraffea, A.R., Eason, T.G., et al.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. 2011, 14 (2011)
Fourgeau, E., Gomez, E., Adli, H., et al.: System engineering workbench for multi-views systems methodology with 3DEXPERIENCE platform. The aircraft RADAR use case. In: Cardin, M.A., Fong, S., Krob, D., Lui, P., Tan, Y. (eds.) Complex Systems Design and Management Asia, pp. 269–270. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29643-2_21
Tao, F., Zhang, M., Cheng, J., et al.: Digital twin workshop: a new paradigm for future workshop. Comput. Integr. Manuf. Syst. 23(1), 1–9 (2017)
Tao, F., Cheng, J., Qi, Q., et al.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9–12), 3563–3576 (2018)
Zhang, Q., Zhang, X., Xu, W., Liu, A., Zhou, Z., Pham, D.T.: Modeling of digital twin workshop based on perception data. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017. LNCS (LNAI), vol. 10464, pp. 3–14. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65298-6_1
Kinsheel, A.: Robust least square estimation of the CRS A465 robot arm’s dynamic model parameters. J. Mech. Eng. Res. 4(3), 89–99 (2012)
Daemi, M., Heimann, B.: Identification and compensation of gear friction for modeling of robots. In: Morecki, A., Bianchi, G., Rzymkowski, C. (eds.) ROMANSY 11: Theory and Practice of Robots and Manipulators, pp. 89–96. Springer, Vienna (1997). https://doi.org/10.1007/978-3-7091-2666-0_11
Acknowledgment
This research is supported by National Natural Science Foundation of China (Grant No. 51775399), the Fundamental Research Funds for the Central Universities (Grant No. 2018III034GX), and the Engineering and Physical Sciences Research Council (EPSRC), UK (Grant No. EP/N018524/1).
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Yan, K., Xu, W., Yao, B., Zhou, Z., Pham, D.T. (2018). Digital Twin-Based Energy Modeling of Industrial Robots. In: Li, L., Hasegawa, K., Tanaka, S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2018. Communications in Computer and Information Science, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-13-2853-4_26
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