Skip to main content
SpringerLink
Log in

Research on Robot Grinding Force Control Method

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13014))

Included in the following conference series:

Abstract

The contact force control between the tool and the workpiece in the industrial robot grinding process is essential to improve the surface quality and processing efficiency of the workpiece. This paper focus on the control of grinding force in the robot grinding process. Firstly, the grinding force control device and method are developed. Then, the force control mathematical model of the system is established and the fuzzy PID strategy of constant force control is especially designed. Finally, the force control tracking experiments has been conducted to verify the system performance. The experimental results show that the contact force control can be controlled smoothly, which can ensure the constant contact force between the workpiece and the tool of the grinding process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ma, Z., Peng, L., Wang, J.: Ultra-smooth polishing of high-precision optical surface. Optik Int. J. Light Electron Optics 124(24), 6586–6589 (2013)

    Article  Google Scholar 

  2. Birkgan, S.E., Bachurin, V.I.: Computer simulation of the two-stage ion polishing of a silicon surface. J. Surf. Invest. X-ray, Synchrotron Neutron Tech. 8(3), 524–529 (2014)

    Google Scholar 

  3. Saito, K.: Finishing and polishing of free-form surface. Bull. Jpn Soc. Precis. Eng. 18(2), 104–109 (1984)

    Google Scholar 

  4. Dieste, J.A., et al.: Automatic grinding and polishing using spherical robot. Procedia Eng. 63, 938–946 (2013)

    Article  Google Scholar 

  5. Xiang, Z., Cabaravdic, M., Kneupner, K., et al.: Real-time simulation of robot controlled belt grinding processes of sculptured surfaces. Int. J. Adv. Rob. Syst. 1(1), 109–114 (2008)

    Google Scholar 

  6. Yu, T.B., Zhang, X., Xu, X.L., et al.: Researches on virtual machining simulation of flexible manufacturing cell based on KUKA robot. Key Eng. Mater. 621, 499–504 (2014)

    Article  Google Scholar 

  7. Zhang, Y., Guo, K., Sun, J.: Investigation on the milling performance of amputating clamping supports for machining with industrial robot. Int. J. Adv. Manuf. Technol. 102(9–12), 3573–3586 (2019)

    Article  Google Scholar 

  8. Saito, K., Miyoshi, T., Sasaki, T.: Automation of polishing process for a cavity surface on dies and molds by using an expert system. CIRP Ann. 42(1), 553–556 (1993)

    Article  Google Scholar 

  9. Peng, W., Guan, C., Li, S.: Material removal mode affected by the particle size in fluid jet polishing. Appl. Opt. 52(33), 7927–7933 (2013)

    Article  Google Scholar 

  10. Kunieda, M., Nakagawa, T., Hiramatsu, H., et al.: A Magnetically Pressed Polishing Tool for a Die Finishing Robot. Macmillan Education (1984)

    Google Scholar 

  11. Hogan, N.: Impedance control: an approach to manipulation: part II—implementation (1985)

    Google Scholar 

  12. Zhang, Y., Guo, K., Sun, J., Sun, Y.: Method of postures selection for industrial robot joint stiffness identification. IEEE Access 9, 62583–62592 (2021)

    Article  Google Scholar 

  13. Mason, M.T.: Compliance and force control for computer controlled manipulators. IEEE Trans. Syst. Man Cybern. 11(6), 418–432 (1981)

    Article  Google Scholar 

  14. Guo, K., Pan, Y., Zheng, D., et al.: Composite learning control of robotic systems: a least squares modulated approach. Automatica 111, 108612 (2020)

    Google Scholar 

  15. Tian, F., et al.: Modeling and control of robotic automatic polishing for curved surfaces. CIRP J. Manuf. Sci. Technol. 14, 55–64 (2016)

    Article  Google Scholar 

  16. Guo, K., Pan, Y., Yu, H.: Composite learning robot control with friction compensation: a neural network-based approach. IEEE Trans. Industr. Electron. 66(10), 7841–7851 (2019)

    Article  Google Scholar 

  17. Shi, Y., et al.: NC polishing of aspheric surfaces under control of constant pressure using a magnetorheological torque servo. Int. J. Adv. Manuf. Technol. 58(9–12), 1061–1073 (2012)

    Article  Google Scholar 

  18. Haibo, Z., et al.: A hybrid control strategy for grinding and polishing robot based on adaptive impedance control. Adv. Mech. Eng. 13(3), 16878140211004034 (2021)

    Google Scholar 

  19. Ahn, J.H., Lee, M.C., Jeong, H.D., Kim, S.R., Cho, K.K.: Intelligently automated polishing for high quality surface formation of sculptured die. J. Mater. Process. Technol. 130–131, 339–344 (2002)

    Article  Google Scholar 

  20. Ahn, J.H., Shen, Y.F., Kim, H.Y., Jeong, H.D., Cho, K.K.: Development of a sensor information integrated expert system for optimizing die polishing. Robot. Comput. Integr. Manuf. 17(4), 269–276 (2001)

    Article  Google Scholar 

  21. Zhou, W., et al.: Development of a real-time force-controlled compliant polishing tool system with online tuning neural proportional–integral–derivative controller. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 229(5), 440–454 (2015)

    Google Scholar 

  22. Brecher, C., et al.: Development of a force controlled orbital polishing head for free form surface finishing. Prod. Eng. Res. Devel. 4(2–3), 269–277 (2010)

    Article  Google Scholar 

  23. Tuell, M.T.: Aspheric optics: smoothing the ripples with semi-flexible tools. Optical Eng. 41(7), 1473 (2002)

    Article  Google Scholar 

  24. Ahn, K.K., Anh, H.: Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator. Mechatronics 19(6), 816–828 (2009)

    Article  Google Scholar 

  25. Xu, Y., Guo, K., Sun, J., et al.: Design, modeling and control of a reconfigurable variable stiffness actuator. Mech. Syst. Signal Process. 160, 107883 (2021)

    Article  Google Scholar 

  26. Xu, Y., Guo, K., Li, J., et al.: A novel rotational actuator with variable stiffness using S-shaped springs. IEEE/ASME Trans. Mechatron. 1, 2249–2260 (2020)

    Google Scholar 

  27. Liao, L., Xi, F.J., Liu, K.: Adaptive control of pressure tracking for polishing process. J. Manuf. Sci. Eng. 132(1), 011015 (2010)

    Article  Google Scholar 

  28. Güvenç, L., Srinivasan, K.: Force controller design and evaluation for robot-assisted die and mould polishing. Mech. Syst. Signal PR 9(1), 31–49 (1995)

    Article  Google Scholar 

  29. Fan, C., Hong, G.S., Zhao, J., Zhang, L., Zhao, J., Sun, L.: The integral sliding mode control of a pneumatic force servo for the polishing process. Precision Eng. 55, 154–170 (2019)

    Article  Google Scholar 

  30. Lu, J., Chen, G., Hao, Y.: Predictive fuzzy PID control: theory, design and simulation. Inf. Sci. 137(1–4), 157–187 (2001)

    Article  MathSciNet  Google Scholar 

  31. Guo, K., Li, M., Shi, W., et al.: Adaptive tracking control of hydraulic systems with improved parameter convergence. IEEE Trans. Ind. Electron. 1 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    MingJian Sun, Kai Guo & Jie Sun

  2. Research Center for Aeronautical Component Manufacturing Technology and Equipment, Shandong University, Jinan, 250061, China

    MingJian Sun, Kai Guo & Jie Sun

Authors
  1. MingJian Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Guo .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Xin-Jun Liu

  2. Tsinghua University, Beijing, China

    Zhenguo Nie

  3. Beihang University, Beijing, China

    Jingjun Yu

  4. Tsinghua University, Beijing, China

    Fugui Xie

  5. Shandong University, Shandong, China

    Rui Song

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sun, M., Guo, K., Sun, J. (2021). Research on Robot Grinding Force Control Method. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_77

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89098-8_77

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89097-1

  • Online ISBN: 978-3-030-89098-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation