A Dynamic Simulation Model of Industrial Robots for Energy Examination Purpose

Paryanto Paryanto; Alexander Hetzner; Matthias Brossog; Jörg Franke

doi:10.4028/www.scientific.net/AMM.805.223

Paper Titles

Energy Demand Simulation of Machine Tools with Improved Chatter Stability Achieved by Active Damping
p.187

Energy Efficiency Analysis of Vapor Phase Soldering Technology through Exergy-Based Metric
p.196

Measurement of the Resource Consumption of a Selective Laser Melting Process
p.205

Economic and Ecological Evaluation of Hybrid Additive Manufacturing Technologies Based on the Combination of Laser Metal Deposition and CNC Machining
p.213

A Dynamic Simulation Model of Industrial Robots for Energy Examination Purpose
p.223

Evaluation of Model Based Predictive Control Algorithms for Fractional Horse Power Drives
p.231

Energetic Simulation of Complex Mechatronic Drive Systems over Complete Drive Cycles
p.241

Simulation-Based Optimization of the Energy Consumption in the Hardening Process for Calcium Silicate Masonry Units
p.249

Improving Resource and Energy Efficiency of Packaging Machines: Contribution of an Increasing Format Flexibility
p.257

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 805A Dynamic Simulation Model of Industrial Robots...

A Dynamic Simulation Model of Industrial Robots for Energy Examination Purpose

Abstract:

In this paper, a modular dynamic model of an industrial robot (IR) for predicting and analyzing its energy consumption is developed. The model consists of control systems, which include a state-of-the-art feedback linearization controller, permanent magnet synchronous drives and the mechanical structure with Coulomb friction and linear damping. By using the developed model, a detailed analysis of the influence of different parameter sets on the energy consumption and loss energy of IRs is investigated. The investigation results show that the operating parameters, robot motor drives, and mechanical damping and elasticity of robot transmissions have a significant effect on the energy consumption and accuracy of IRs. However, these parameters are not independent, but rather interrelated. For example, a higher acceleration and velocity shortens IRs’ operating periods, but needs a greater motor current, tends to excite vibrations to a greater extent, and thus produces a higher amount of loss energy.

You might also be interested in these eBooks

Energy Efficiency in Strategy of Sustainable Production

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 805)

Pages:

223-230

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.805.223

Citation:

Cite this paper

Online since:

November 2015

Authors:

Paryanto Paryanto*, Alexander Hetzner, Matthias Brossog, Jörg Franke

Keywords:

Control Systems, Energy Consumption, Industrial Robots, Mechatronic Simulation, Modelica

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] Paryanto, M. Brossog, M. Bornschlegl, J. Franke, Reducing the energy consumption of industrial robots in manufacturing systems, Int. J. Adv. Manuf. Tech. 78(5-8) (2015) 1315-1328.

DOI: 10.1007/s00170-014-6737-z

Google Scholar

[2] E.S. Sergaki, G.S. Stavrakakis, A.D. Pouliezos, Optimal robot speed trajectory by minimization of the actuator motor electromechanical losses, J. Intell. Robot Syst. 33(2) (2002) 187–207.

Google Scholar

[3] Paryanto, M. Brossog, J. Kohl, J. Merhof, S. Spreng, J. Franke, Energy consumption and dynamic behavior analysis of a six-axis industrial robot in an assembly system, Procedia CIRP 23 (2014), 131-136.

DOI: 10.1016/j.procir.2014.10.091

Google Scholar

[4] A. Rassolkin, H. Hõimoja, R. Teemets, Energy saving possibilities in the industrial robot IRB 1600 control. Proc. of the 7th Intl. Conf. -Works. CPE, (2011) 226-229.

DOI: 10.1109/cpe.2011.5942236

Google Scholar

[5] M. Thümmel, M. Otter, J. Bals, Control of robots with elastic joints based on automatic generation of inverse dynamics models. Proc. of the IEEE/RSJ Intl. Conf. Intll. Robots and Syst. (2001), 925-930.

DOI: 10.1109/iros.2001.976287

Google Scholar

[6] P. Fritzson, Principles of object-oriented modeling and simulation with Modelica 2. 1, Wiley-IEEE Press (2004).

Google Scholar

[7] A.B. Dehkordi, A.M. Gole, T.L. Maguire, Permanent magnet synchronous machine model for real-time simulation. Proc. of the Intl. Conf. on Power Sys. Transients (2005).

Google Scholar

[8] B. Zigmund, A. Terlizzi, X.T. Garcia, R. Pavlanin, L. Salvatore, Experimental evaluation of PI tuning techniques for field oriented control of permanent magnet synchronous motors. Adv. in Elec. and Electro. Eng. 5(1-2), 114–119.

Google Scholar

[9] F.L. Lewis, D.M. Dawson, C.T. Abdallah, Robot manipulator control: Theory and practice. Marcel Dekker, Inc (2004).

Google Scholar

[10] R. Kelly, V.S. Davila, A. Loria, Control of robot manipulators in joint space. Springer Science & Business Media (2005).

Google Scholar

[11] T. Hardeman, Modelling and identification of industrial robots including drive and joint flexibilities. PhD Dissertation, University of Twente (2008).

Google Scholar

[12] M. Chemnitz, G. Schreck, J. Kruger, Analyzing energy consumption of industrial robots. Proc. of IEEE 16th Conference on EFTA (2011) 1-4.

DOI: 10.1109/etfa.2011.6059221

Google Scholar

[13] Paryanto, M. Brossog, J. Merhof, J. Franke, Mechatronic behavior analysis of customized manufacturing cell. Lect. Notes Prod. Eng. (2014), 389-399.

DOI: 10.1007/978-3-319-04271-8_33

Google Scholar

[14] S. Kreitlein, A. Höft, S. Schwender, J. Franke, Green Factories Bavaria: A network of distributed learning factories for energy efficient production. Procedia CIRP 32 (2015), 58-63.

DOI: 10.1016/j.procir.2015.02.219

Google Scholar