Offline simulation of path deviation due to joint compliance and hysteresis for robot machining

  • Marcel CordesEmail author
  • Wolfgang Hintze


Industrial robots offer a cost-effective and flexible machining alternative to the classic machining centers. One disadvantage is the significantly reduced working accuracy that results in considerable path deviation, particularly under load. To maximize the effectiveness of the robot and to derive suitable machining strategies, knowledge of the effects on the working accuracy is required. In this study, a model is presented in joint space that predicts the path deviation on the basis of joint stiffness and reversal error with high accuracy, with respect to the kinematic model and path planning. The identification of the model parameters of individual joints is carried out without disassembling the robot. The model is validated against the hysteresis occurring at the Tool Center Point and through the milling of circular contours. It is shown that the reversal error is mainly caused by hysteresis and not by backlash at zero crossing. The subsequent offline compensation strategy allows a considerable reduction of dimension and form deviations.


Robot Machining Joint compliance Hysteresis Reversal error 


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  1. 1.
    Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf Technol 66(9-12):1489–1497CrossRefGoogle Scholar
  2. 2.
    Slamani M, Gauthier S, Chatelain J-F (2015) A study of the combined effects of machining parameters on cutting force components during high speed robotic trimming of cfrps. Measurement 59:268–283CrossRefGoogle Scholar
  3. 3.
    Ruderman M, Hoffmann F, Bertram T (2009) Modeling and identification of elastic robot joints with hysteresis and backlash. IEEE Trans Ind Electron 56(10):3840–3847CrossRefGoogle Scholar
  4. 4.
    Mejri S, Gagnol V, Le T-P, Sabourin L, Ray P, Paultre P (2016) Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol 82(1-4):351–359CrossRefGoogle Scholar
  5. 5.
    Pan Z, Zhang H, Zhu Z, Wang J (2006) Chatter analysis of robotic machining process. J Mater Process Technol 173(3):301–309CrossRefGoogle Scholar
  6. 6.
    Schneider U, Drust M, Ansaloni M, Lehmann C, Pellicciari M, Leali F, Gunnink JW, Verl A (2016) Improving robotic machining accuracy through experimental error investigation and modular compensation. Int J Adv Manuf Technol 85(1-4):3–15CrossRefGoogle Scholar
  7. 7.
    Tratar J, Pusavec F, Kopac J (2014) Tool wear in terms of vibration effects in milling medium-density fibreboard with an industrial robot. J Mech Sci Technol 28(11):4421–4429CrossRefGoogle Scholar
  8. 8.
    Wojciechowski S, Chwalczuk T, Twardowski P, Krolczyk GM (2015) Modeling of cutter displacements during ball end milling of inclined surfaces. Arch Civ Mech Eng 15(4):798–805CrossRefGoogle Scholar
  9. 9.
    Franco P, Estrems M, Faura F (2008) A study of back cutting surface finish from tool errors and machine tool deviations during face milling. Int J Mach Tools Manuf 48(1):112–123CrossRefGoogle Scholar
  10. 10.
    Pashkevich A, Klimchik A, Chablat D (2011) Enhanced stiffness modeling of manipulators with passive joints. Mech Mach Theory 46(5):662–679CrossRefzbMATHGoogle Scholar
  11. 11.
    Wang J, Zhang H, Fuhlbrigge T (2009) Improving machining accuracy with robot deformation compensation, The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3826–3831Google Scholar
  12. 12.
    Slavkovic NR, Milutinovic DS, Glavonjic MM (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):2083–2096CrossRefGoogle Scholar
  13. 13.
    Abele E, Weigold M, Rothenbücher S (2007) Modeling and identification of an industrial robot for machining applications. CIRP Ann Manuf Technol 56(1):387–390CrossRefGoogle Scholar
  14. 14.
    Olabi A, Damak M, Bearee R, Gibaru O, Leleu S (2012) Improving the accuracy of industrial robots by offline compensation of joints errors, IEEE International Conference on Industrial Technology (ICIT 2012), 492–497Google Scholar
  15. 15.
    Alici G, Shirinzadeh B (2005) Enhanced stiffness modeling, identification and characterization for robot manipulators. IEEE Trans Robot 21(4):554–564CrossRefzbMATHGoogle Scholar
  16. 16.
    Dumas C, Caro S, Chérif M., Garnier S, Furet B (2010) A methodology for joint stiffness identification of serial robots, IEEE/RSJ International Conference on Intelligent Robots and Systems, 464–469Google Scholar
  17. 17.
    Taek Oh Y (2011) Influence of the joint angular characteristics on the accuracy of industrial robots. Ind Robot: An Int J 38(4):406–418CrossRefGoogle Scholar
  18. 18.
    Kircanski NM, Goldenberg AA (1997) An experimental study of nonlinear stiffness, hysteresis, and friction effects in robot joints with harmonic drives and torque sensors. Int J Robot Res 16(2):214–239CrossRefGoogle Scholar
  19. 19.
    Slamani M, Bonev IA (2013) Characterization and experimental evaluation of gear transmission errors in an industrial robot. Ind Robot: An Int J 40(5):441–449CrossRefGoogle Scholar
  20. 20.
    Xiao Y, Du Z, You W, Li R (2010) Modeling and simulating the nonlinear characters of robot joints, IEEE International Conference on Robotics and Biomimetics (ROBIO), 914–919Google Scholar
  21. 21.
    Freising M, Kothe S, Rott M, Susemihl H, Hintze W (2013) Increasing accuracy of industrial robots in machining of carbon fiber reinforced plastics, Proceedings of the 4th Machining Innovations Conference, 115–121Google Scholar
  22. 22.
    Lightcap C, Hamner S, Schmitz T, Banks S (2008) Improved positioning accuracy of the pa10-6ce robot with geometric and flexibility calibration. IEEE Trans Robot 24(2):452–456CrossRefGoogle Scholar
  23. 23.
    Nubiola A, Bonev IA (2013) Absolute calibration of an abb irb 1600 robot using a laser tracker. Robot Comput Integr Manuf 29(1):236–245CrossRefGoogle Scholar
  24. 24.
    Mustafa SK, Tao PY, Yang G, Chen I-M (2010) A geometrical approach for online error compensation of industrial manipulators, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2010), 738–743Google Scholar
  25. 25.
    Reinl C, Friedmann M, Bauer J, Pischan M, Abele E, von Stryk O (2011) Model-based off-line compensation of path deviation for industrial robots in milling applications, IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2011), 367–372Google Scholar
  26. 26.
    Abele E, Schützer K., Bauer J, Pischan M (2012) Tool path adaption based on optical measurement data for milling with industrial robots. Prod Eng 6(4-5):459–465CrossRefGoogle Scholar
  27. 27.
    Zaeh MF, Roesch O (2014) Improvement of the machining accuracy of milling robots. Prod Eng 8(6):737–744CrossRefGoogle Scholar
  28. 28.
    Corke P, Siciliano B, Khatib O (2011) Robotics vision and control. Springer, BerlinCrossRefGoogle Scholar
  29. 29.
    Craig JJ (2005) Introduction to robotics: mechanics and control 3rd ed. Upper Saddle River and N.J: Pearson/Prentice HallGoogle Scholar
  30. 30.
    Chen S-F (2003) The 6×6 stiffness formulation and transformation of serial manipulators via the cct theory, IEEE International Conference on Robotics and Automation. IEEE ICRA 2003 Conference Proceedings, 4042–4047Google Scholar

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© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.Institute of Production Management and TechnologyHamburg University of TechnologyHamburgGermany

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