Nowadays, industrial robots could be a successful alternative to machine tools for milling of large parts with complex geometry. As it is known, poor accuracy which is most influenced by the stiffness of robot structure is recognized as a limiting factor for successful use of robots in milling tasks. Since there are different sources of error in robots, virtual manufacturing systems provide a useful means for products to be manufactured without the need of physical testing on the shop floor. This paper presents the developed virtual robot machining model, as a part of digital twin, for the simulation of a modified tool path generated by the compensation algorithm for the errors induced by cutting forces due to robot compliance. This part of digital twin includes the robot kinematic model, the Cartesian space robot compliance model, the model of cutting forces and the developed program based on off-line compensation algorithm. Development of such virtual robot machining model is important for the validation of modified tool path before the machining on a real robot. The developed virtual robot machining model is verified via several experiments, where the simulated surfaces are compared with the real machined surfaces generated by tool movement through a modified, i.e., corrected, trajectory on an available industrial robot programmed in G-code.
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Armendia M, Alzaga A, Peysson F, Fuertjes T, Cugnon F, Ozturk E, Flum D (2019) Machine tool: from the digital twin to the cyber-physical systems. In: Armendia M, Ghassempouri M, Ozturk E, Peysson F (eds) Twin-control: a digital twin approach to improve machine tools lifecycle. Springer, Cham, pp 3–21. https://doi.org/10.1007/978-3-030-02203-7
Kagermann H, Wolfgang W, Helbi J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0 Working Group
Kadir AA, Xu X, Hämmerle E (2011) Virtual machine tools and virtual machining-A technological review. Robot Comput-Integr Manuf 27(3):494–508. https://doi.org/10.1016/j.rcim.2010.10.003
Liu J, Zhou H, Liu X, Tian G, Wu M, Cao L, Wang W (2019) Dynamic evaluation method of machining process planning based on digital twin. IEEE Access 7:19312–19323. https://doi.org/10.1109/ACCESS.2019.2893309
Luo W, Hu T, Zhang C, Wei Y (2018) Digital twin for CNC machine tool: modeling and using strategy. J Ambient Intell Humaniz Comput 10(3):1129–1140. https://doi.org/10.1007/s12652-018-0946-5
Scaglioni B, Ferretti G (2018) Towards digital twins through object-oriented modelling: a machine tool case study. IFAC Pap Line 51(2):613–618. https://doi.org/10.1016/j.ifacol.2018.03.104
Guerra RH, Quiza R, Villalonga A, Arenas J, Castano F (2019) Digital twin-based optimization for ultraprecision motion systems with backlash and friction. IEEE Access 7:93462–93472. https://doi.org/10.1109/ACCESS.2019.2928141
Altintas Y, Brecher C, Weck M, Witt S (2005) Virtual machine tool. CIRP Ann 54(2):115–138. https://doi.org/10.1016/S0007-8506(07)60022-5
Slavkovic N, Milutinovic D, Glavonjic M (2014) A method for off-line compensation of cutting force-induced errors in robotic machining by tool path modification. Int J Adv Manuf Technol 70(9–12):2083–2096. https://doi.org/10.1007/s00170-013-5421-z
Brüning J, Denkena B, Dittrich MA, Park HS (2016) Simulation based planning of machining processes with industrial robots. Procedia Manuf 6:17–24. https://doi.org/10.1016/j.promfg.2016.11.003
DePree J, Gesswein C (2008) Robotic machining white paper project-Halcyon Development. Robotic Industries Association. https://www.robotics.org/robotics/halcyon-development-ria. Accessed 28 March 2019
Klimchik A, Pashkevich A, Chablat D, Hovland G (2013) Compliance error compensation technique for parallel robots composed of nonperfect serial chains. Robot Comput-Integr Manuf 29(2):385–393. https://doi.org/10.1016/j.rcim.2012.09.008
Milutinovic D, Glavonjic M, Slavkovic N, Dimic Z, Zivanovic S, Kokotovic B, Lj Tanovic (2011) Reconfigurable robotic machining system controlled and programmed in a machine tool manner. Int J Adv Manuf Technol 53(9–12):1217–1229. https://doi.org/10.1007/s00170-010-2888-8
Abele E, Weigold M, Rothenbucher S (2007) Modeling and identification of an industrial robot for machining applications. CIRP Ann Manuf Technol 56(1):387–390. https://doi.org/10.1016/j.cirp.2007.05.090
Pan Z, Zhang H (2008) Robotic machining from programming to process control: a complete solution by force control. Ind Robot Int J 35(5):400–409. https://doi.org/10.1108/01439910810893572
Lehmann C, Pellicciari M, Drust M, Gunnink JW (2013) Machining with industrial robots: the COMET project approach. In: Neto P, Moreira AP (eds) Robotics in smart manufacturing. Springer, Berlin, pp 27–36
Alvares AJ, Toquica JS, Lima EJ II, Bomfim MHS (2018) Retrofitting of the IRB6-S2 robotic manipulator using computer numerical control- based controllers. J Braz Soc Mech Sci Eng 40:149. https://doi.org/10.1007/s40430-018-1073-0
Rosa DGG, Feiteira JFS, Lopes AM, Abreu PAF (2017) Analysis and implementation of a force control strategy for drilling operations with an industrial robot. J Braz Soc Mech Sci Eng 39:4749–4756. https://doi.org/10.1007/s40430-017-0913-7
Slavkovic N, Milutinovic D, Kokotovic B, Glavonjic M, Zivanovic S, Ehmann K (2013) Cartesian compliance identification and analysis of an articulated machining robot. FME Trans 41(2):83–95
Alici G, Shirinzadeh B (2005) Enhanced stiffness modeling, identification and characterization for robot manipulators. IEEE Trans Robot 21(4):554–564. https://doi.org/10.1109/TRO.2004.842347
Abele E, Schutzer K, Bauer J, Pischan M (2012) Tool path adaptation based on optical measurement data for milling with industrial robots. Prod Eng Res Dev 6(4–5):459–465. https://doi.org/10.1007/s11740-012-0383-9
Dumas C, Caro S, Garnier S, Furet B (2011) Joint stiffness identification of six-revolute industrial serial robots. Robot Comput-Integr Manuf 27(4):881–888. https://doi.org/10.1016/j.rcim.2011.02.003
Soori M, Arezoo B, Habibi M (2014) Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines. J Manuf Syst 33(4):498–507. https://doi.org/10.1016/j.jmsy.2014.04.007
Abele E, Rothenbucher S, Weigold M (2008) Cartesian compliance model for industrial robots using virtual joints. Prod Eng Res Dev 2(3):339–343. https://doi.org/10.1007/s11740-008-0118-0
Abele E, Bauer J, Rothenbucher S, Stelzer M, Stryk O (2008) Prediction of the tool displacement by coupled models of the compliant industrial robot and the milling process. In: Proceedings of the international conference on process machine interactions 223–230 Hannover. https://pdfs.semanticscholar.org/cb38/78151645e30d6b46c2c65f8293184ab45f5e.pdf. Accessed 28 March 2019
Gu J, Agapiou JS, Kurgin S (2017) Error compensation and accuracy improvements in 5-axis machine tools using the global offset method. J Manuf Syst 44(2):324–331. https://doi.org/10.1016/j.jmsy.2017.04.015
Slavkovic N (2015) Identification, modelling and compensation of errors due to machining robot static compliance. Dissertation, University of Belgrade
Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations and CNC design. Cambridge University Press, Cambridge
Budak E (2006) Analytical models for high performance milling. PartI: cutting forces, structural deformations, and tolerance integrity. Int J Mach Tools Manuf 46(12–13):1478–1488. https://doi.org/10.1016/j.ijmachtools.2005.09.009
Kokotovic B (2013) Milling in a virtual manufacturing system. Dissertation, University of Belgrade
Gajate A, Haber RE, Vega PI, Alique JR (2010) A transductive neuro-fuzzy controller: application to a drilling process. IEEE Trans Neural Netw 21(7):1158–1167. https://doi.org/10.1109/TNN.2010.2050602
Haber RE, Alique JR (2004) Nonlinear internal model control using neural networks: an application for machining processes. Neural Comput Appl 13:47–55. https://doi.org/10.1007/s00521-003-0394-8
Del Toro RM, Schmittdiel MC, Haber-Guerra RE, Haber-Haber R (2007) System identification of the high performance drilling process for network-based control. In: Proceedings of the ASME 2007 international design engineering technical conferences & computers and information in engineering conference IDETC/CIE 2007. Las Vegas, Nevada, USA, pp 827–834
Beruvidesa G, Quiza R, Habera RE (2016) Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process. Inf Sci 334–335:161–173. https://doi.org/10.1016/j.ins.2015.11.040
Fu KS, Gonzalez R, Lee CSG (1987) Introduction to robotics: control, sensing, vision, and intelligence. McGraw-Hill, New York
Craig JJ (1989) Introduction to robotics: mechanics and control, 2nd edn. Addison-Wesley, Massachusetts
ISO 841:2001 Industrial automation systems and integration—Numerical control of machines—coordinate system and motion nomenclature
This work was supported by the Ministry of Education, Science and Technological Development of Serbia (Development of a new generation of domestic manufacturing systems—TR 35022), Republic of Serbia.
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Slavkovic, N., Zivanovic, S., Kokotovic, B. et al. Simulation of compensated tool path through virtual robot machining model. J Braz. Soc. Mech. Sci. Eng. 42, 374 (2020). https://doi.org/10.1007/s40430-020-02461-9
- Virtual model
- Robot machining
- Compliance analysis
- Off-line compensation