A controllable material removal strategy considering force-geometry model of belt grinding processes



Belt grinding is commonly used in the process of machining complex surface. However, due to the elasticity of the grinding belt, it needs repeated or longer dwell-time grinding in order to meet the required machining precision, which is inefficient, time-consuming, and always ended up with poor surface quality. So, this paper focuses on a machining method so as to improve machining efficiency and accuracy. First, considering the elastic deformation of the contact wheel and characteristics of the workpiece, the global and local material removal processes of belt grinding are modeled to calculate the acting force. Then, based on the analysis of rigid-flexible coupling, a controlling strategy is proposed to control the acting force and grinding dwell time. The variable feed grinding experiments were carried out on the developed five-axis CNC belt grinding machine integrated with measuring and machining. The ladder type workpiece surface and free-form workpiece surface were employed to validate the proposed controllable material removal strategy. The results verify that the proposed strategy is feasible and efficient.


Belt grinding Material removal Force-geometry model 


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  1. 1.
    Yun H, Zhi H (2009) Modern belt grinding technology and engineering applicationsGoogle Scholar
  2. 2.
    Park JW, Cho HU, Chung CW, Lee YS, Jeon DJ (2012) Modeling and grinding large sculptured surface by robotic digitization. J Mech Sci Technol 26(7):2087–2091CrossRefGoogle Scholar
  3. 3.
    Liu, R. J., Huang, Y., Huang, Z., Lv, Z. M., & Jin, X. X. (2010) Simulation and experimental research of adaptive control with constant pressure on large-scale titanium alloy composite plates. In Advanced Materials Research (Vol. 148, pp. 399-405). Trans Tech PublicationsGoogle Scholar
  4. 4.
    Schinhaerl M, Smith G, Stamp R, Rascher R, Smith L, Pitschke E, Sperber P, Geiss A (2008) Mathematical modeling of influence functions in computer-controlled polishing: part I & part II. Appl Math Model 32(12):2888–2924CrossRefGoogle Scholar
  5. 5.
    Wang G, Wang Y, Xu Z (2009) Modeling and analysis of the material removal depth for stone polishing. J Mater Process Technol 209(5):2453–2463CrossRefGoogle Scholar
  6. 6.
    Zhang X, Kuhlenkötter B, Kneupner K (2005) An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding. Int J Mach Tool Manuf 45(6):641–648CrossRefGoogle Scholar
  7. 7.
    Zhang X, Kneupner K, Kuhlenkötter B (2006) A new force distribution calculation model for high-quality production processes. Int J Adv Manuf Technol 27(7-8):726–732CrossRefGoogle Scholar
  8. 8.
    Ren X, Kuhlenkötter B, Müller H (2006) Simulation and verification of belt grinding with industrial robots. Int J Mach Tool Manuf 46(7):708–716CrossRefGoogle Scholar
  9. 9.
    Ren X, Kuhlenkötter B (2008) Real-time simulation and visualization of robotic belt grinding processes. Int J Adv Manuf Technol 35(11-12):1090–1099CrossRefGoogle Scholar
  10. 10.
    Ren X, Cabaravdic M, Zhang X, Kuhlenkötter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tool Manuf 47(6):962–970CrossRefGoogle Scholar
  11. 11.
    Wu S, Kazerounian K, Gan Z, Sun Y (2013) A simulation platform for optimal selection of robotic belt grinding system parameters. Int J Adv Manuf Technol 64(1-4):447–458CrossRefGoogle Scholar
  12. 12.
    Lee H, Yang M (2001) Dwell time algorithm for computer-controlled polishing of small axis-symmetrical aspherical lens mold. Opt Eng 40(9):1936–1943CrossRefGoogle Scholar
  13. 13.
    Kim DW, Kim SW, Burge JH (2009) Non-sequential optimization technique for a computer controlled optical surfacing process using multiple tool influence functions. Opt Express 17(24):21850–21866CrossRefGoogle Scholar
  14. 14.
    Wang C, Yang W, Wang Z, Yang X, Hu C, Zhong B, Guo Y, Xu Q (2014) Dwell-time algorithm for polishing large optics. Appl Optics 53(21):4752–4760CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Wang, Y., Wang, Y., He, J., Ji, F., & Huang, W. (2010) Dwell time algorithm based on the optimization theory for magnetorheological finishing. In 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies (pp. 76551V-76551V). International Society for Optics and Photonics.Google Scholar
  16. 16.
    Fan, B., Burge, J. H., Martin, H., Zeng, Z., Li, X., & Zhou, J. (2012) Dwell time calculation for computer controlled large tool. In 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2012) (pp. 84150A-84150A). International Society for Optics and Photonics.Google Scholar
  17. 17.
    Song C, Dai Y, Peng X (2010) Model and algorithm based on accurate realization of dwell time in magnetorheological finishing. Appl Optics 49(19):3676–3683CrossRefGoogle Scholar
  18. 18.
    Dong Z, Cheng H, Tam HY (2014) Modified dwell time optimization model and its applications in subaperture polishing. Appl Optics 53(15):3213–3224CrossRefGoogle Scholar
  19. 19.
    Jiang C, Song Q, Guo D, Li H (2014) Estimation algorithm of minimum dwell time in precision cylindrical plunge grinding using acoustic emission signal. Int J Precision Eng Manuf 15(4):601–607CrossRefGoogle Scholar
  20. 20.
    Preston FW (1927) Glass technology. J Soc Glass Technol 11:277–281Google Scholar
  21. 21.
    Wang, Y. J., Huang, Y., Chen, Y. X., & Yang, Z. S. (2015). Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Technol, 1-10Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Yongqing Wang
    • 1
  • Bo Hou
    • 1
  • Fengbiao Wang
    • 1
  • Zhichao Ji
    • 1
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of EducationDalian University of TechnologyDalianChina

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