A numerical model to investigate contact status for rail grinding by abrasive belt with an axial deflection

  • Wenxi Wang
  • Jianyong Li
  • Wengang FanEmail author
  • Guangyou Hou
Technical Paper


In order to calculate analytically the contact status for rail grinding by abrasive belt with an axis deflection, as well as considering limitations of using Hertz contact theory for that, a numerical contact model (NCM) based on the local geometrical relationship between contact wheel and rail surface was developed. The effectiveness and accuracy of the proposed model have been validated by the corresponding finite element model (FEM) by comparing the contact boundary and contact stress distribution. Comparative cases at two typical grinding positions with different attitudes were involved, and the effects of applying axis deflection on contact behavior were investigated in detail. The results from NCM and FEM both agreed belt grinding with an axis deflection was able to enlarge the contact width of the belt and the total grinding force under the premise of ensuring required width of grinding trace on the rail. The model provides insights into understanding contact behavior under actual working conditions and gives a theoretical basis for future researches based on contact analysis.


Numerical model Contact Rail grinding Belt grinding 



This work was supported by the Fundamental Research Funds for the Central Universities [Grant Number 2018JBZ105].


  1. 1.
    Liu YM, Yang TY, He Z, Li JY (2018) Analytical modeling of grinding process in rail profile correction considering grinding pattern. Arch Civ Mech Eng 18(2):669–678. CrossRefGoogle Scholar
  2. 2.
    Zhi SD, Li JY, Zarembski AM (2015) Grinding motor energy saving method based on material removal model in rail grinding processes. Int J Precis Eng Manuf-Green Technol 2(1):21–30. CrossRefGoogle Scholar
  3. 3.
    Zhi SD, Li JY, Zarembski AM (2016) Predictive modeling of the rail grinding process using a distributed cutting grain approach. Proc Inst Mech Eng Part F-J Rail Rapid Transit 230(6):1540–1560. CrossRefGoogle Scholar
  4. 4.
    Fan WG, Cheng JF, Lu HB, Li JY, Song XY (2018) Research on time-varying contact behavior and simulation for waved rail surface grinding by abrasive belt. J Mech Eng 54(4):87–92CrossRefGoogle Scholar
  5. 5.
    He Z, Li JY, Liu YM, Nie M, Fan WG (2017) Investigating the effects of contact pressure on rail material abrasive belt grinding performance. Int J Adv Manuf Technol 93(1–4):779–786. CrossRefGoogle Scholar
  6. 6.
    Xiao GJ, He Y, Huang Y, Li Q (2019) Shark-skin-inspired micro-riblets forming mechanism of TC17 titanium alloy with Belt grinding. IEEE Access 7(1):107636–107648. CrossRefGoogle Scholar
  7. 7.
    Wang WX, Salvatore F, Rech J, Li JY (2018) Investigating adhesion wear on belt and its effects on dry belt finishing. J Braz Soc Mech Sci 40(12):570. CrossRefGoogle Scholar
  8. 8.
    Xiao GJ, Huang Y (2015) Constant-load adaptive belt polishing of the weak-rigidity blisk blade. Int J Adv Manuf Technol 78(9–12):1473–1484. CrossRefGoogle Scholar
  9. 9.
    Cheng C, Li JY, Liu YM, Nie M, Wang WX (2019) Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding. Comput Ind 106:1–13. CrossRefGoogle Scholar
  10. 10.
    Liu Z (2013) The design of rail milling train grinding equipment and the research of the grinding force control. Dissertation, Central South UniversityGoogle Scholar
  11. 11.
    Rech J, Kermouche G, Grzesik W, García-Rosales C, Khellouki A, García-Navas V (2008) Characterization and modelling of the residual stresses induced by belt finishing on a AISI52100 hardened steel. J Mater Process Technol 1(1):567–570. CrossRefGoogle Scholar
  12. 12.
    Coste C, Falcon E, Fauve S (1997) Solitary waves in a chain of beads under Hertz contact. Phys Rev E 56(56):6104–6117. CrossRefGoogle Scholar
  13. 13.
    Wang W, Liu F, Liu ZH, Yun C (2017) Prediction of depth of cut for robotic belt grinding. Int J Adv Manuf Technol 91(1–4):699–708. CrossRefGoogle Scholar
  14. 14.
    Wang YQ, Hou B, Wang FB, Ji ZC (2017) A controllable material removal strategy considering force-geometry model of belt grinding processes. Int J Adv Manuf Technol 93(1–4):241–251. CrossRefGoogle Scholar
  15. 15.
    Xiao GJ, Huang Y (2017) Adaptive belt precision grinding for the weak rigidity deformation of blisk leading and trailing edge. Adv Mech Eng 9(10):1–12. CrossRefGoogle Scholar
  16. 16.
    Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. CrossRefGoogle Scholar
  17. 17.
    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 Tools Manuf 45(6):641–648. CrossRefGoogle Scholar
  18. 18.
    Ren X, Cabaravdic M, Zhang X, Kuhlenkötter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf 47(6):962–970. CrossRefGoogle Scholar
  19. 19.
    Ren X, Kuhlenkötter B, Müller H (2006) Simulation and verification of belt grinding with industrial robots. Int J Mach Tools Manuf 46(7):708–716. CrossRefGoogle Scholar
  20. 20.
    Wang YJ, Huang Y, Chen YX, Yang ZS (2016) Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Technol 82(9–12):2113–2122. CrossRefGoogle Scholar
  21. 21.
    Sun Y, Vu TT, Halil Z, Yeo SH (2017) Pressure distribution of serrated contact wheels—experimental and numerical analysis. Int J Adv Manuf Technol 90(9–12):3407–3419. CrossRefGoogle Scholar
  22. 22.
    Wang WX, Li JY, Fan WG (2019) Investigation into static contact behavior in belt rail grinding using a concave contact wheel. Int J Adv Manuf Technol 101(9–12):2825–2835. CrossRefGoogle Scholar
  23. 23.
    Zhang X, Kneupner K, Kuhlenkotter B (2006) A new force distribution calculation model for high-quality production processes. Int J Mach Tools Manuf 27(7–8):726–732. CrossRefGoogle Scholar
  24. 24.
    He Z, Li JY, Liu YM, Yan JW (2019) Investigation on wear modes and mechanisms of abrasive belts in grinding of U71Mn steel. Int J Adv Manuf Technol 101(5–8):1821–1835. CrossRefGoogle Scholar
  25. 25.
    Zhang XQ, Chen HB, Xu JJ, Song XF, Wang JW, Chen XQ (2018) A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine. J Mater Process Tech 260:9–19. CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  • Wenxi Wang
    • 1
  • Jianyong Li
    • 1
    • 2
  • Wengang Fan
    • 1
    • 2
    Email author
  • Guangyou Hou
    • 1
  1. 1.School of Mechanical, Electronic and Control EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control TechnologyMinistry of EducationBeijingChina

Personalised recommendations