Skip to main content
Log in

Dynamic contact modeling considering local material deformation by grit indentation for abrasive belt rail grinding

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

During the operation of the railway system, various defects that occurred on the rail surface significantly affect the service life of the rail and undermine the stability, safety, and passenger comfort of the train. For this problem, abrasive belt rail grinding (ABRG) has obtained increasingly rapid development for the daily rail maintenance due to the merits of efficient grinding, elastic grinding, and cold grinding. In the process of ABRG, the contact behavior between the abrasive belt and rail is the main factor for material removal. However, current researches on contact behavior concentrated in the contact area and pressure distribution under the static state having ignored the role of dynamic abrasives when considering material deformation. Therefore, this paper proposed a mechanical model of single and multiple grits grinding for ABRG based on local material deformation induced by grit indentation, in which the credibility of the model was verified by the dynamic contact simulation based on the particle hydrodynamics-finite element method (SPH-FEM).

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Cannon DF, Edel KO, Grassie SL, Sawley K (2003) Rail defects: an overview. Fatigue Fract Eng Mater Struct 26(10):865–886

    Article  Google Scholar 

  2. Steenbergen M (2016) Rolling contact fatigue in relation to rail grinding. Wear 356-357:110–121

    Article  Google Scholar 

  3. Vigneashwara P, Wahyu C, Tegoeh T, Gunasekaran P (2017) Predictive modelling and analysis of process parameters on material removal characteristics in abrasive belt grinding process. Appl Sci 7(363):1–17

    Google Scholar 

  4. Vigneashwara P, Wahyu C, Tegoeh T, Hock HT (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

    Article  Google Scholar 

  5. Wang JW, Xu JJ, Zhang XQ, Ren XK, Song XF, Chen XQ (2018) An investigation of surface corrosion behavior of Inconel 718 after robotic belt grinding. Materials 11(2440):1–14

    Google Scholar 

  6. Wang JW, Xu JJ, Wang XF, Zhang XQ, Song XF, Chen XQ (2019) A comprehensive study on surface integrity of nickel-based superalloy Inconel 718 under robotic belt grinding. Mater Manuf Process 34(1):61–69

    Article  Google Scholar 

  7. Qi JD, Chen B (2019) Elastic-contact-based tool-path planning for free-form surface in belt grinding. Adv Mech Eng 11(1):1–13

    Article  Google Scholar 

  8. Huang Y, Jiahua SL, Xiao GJ, He Y, Dai WT, He S, Li W (2020) Study on the surface topography of the vibration-assisted belt grinding of the pump gear. Int J Adv Manuf Technol 106(11):719–729

    Article  Google Scholar 

  9. Huang Y, He S, Xiao GJ, Li W, Jiahua SL, Wang WX (2020) Effects research on theoretical-modelling based suppression of the contact flutter in blisk belt grinding. J Manuf Process 54:309–317

    Article  Google Scholar 

  10. Xiao GJ, Huang Y (2019) Surface reconstruction of laser-cladding remanufacturing blade using in adaptive belt grinding. Int J Adv Manuf Technol 101(9–12):3199–3211

    Article  Google Scholar 

  11. Eric EM, Joseph K (2002) The application of contact mechanics to rail profile design and rail grinding. Wear 253(1):308–316

    Google Scholar 

  12. Fan WG, Liu YM, Li JY (2018) Development status and prospect of rail grinding technology for high speed railway. Chin J Mech Eng 54(22):184–193

    Article  Google Scholar 

  13. Real JI, Zamorano C, Velarte JL, Blanco AE (2015) Development of a vehicle–track interaction model to predict the vibratory benefits of rail grinding in the time domain. J Mod Transport 23(3):189–201

    Article  Google Scholar 

  14. Wang WX, Li JY, Fan WG, Hou GY (2019) A numerical model to investigate contact status for rail grinding by abrasive belt with an axial deflection. J Braz Soc Mech Sci 41(11):1–10

    Google Scholar 

  15. 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):2825–2835

    Article  Google Scholar 

  16. Fan WG, Liu YM, Wang WX, Li JY, Wang RQ (2018) Research on modeling method of material removal for rail grinding by abrasive belt based on elastic Hertzian contact. Chin J Mech Eng 54(15):191–198

    Article  Google Scholar 

  17. Fan WG, Wang WX, Hou GY, Wang XH (2020) Macro contact pressure modeling and simulation for rail grinding with abrasive belt based on curvature match. Chin J Mech Eng 56(2):154–162

    Article  Google Scholar 

  18. Fan WG, Liu YM, Song XY, Cheng JF, Li JY (2018) Influencing mechanism of rubber wheel on contact pressure and metal removal in corrugated rail grinding by abrasive belt. J Manuf Sci E-T ASME 140(12):1–8

    Article  Google Scholar 

  19. Wang WX, Li JY, Fan WG, Song XY, Wang LF (2017) Characteristic quantitative evaluation and stochastic modeling of surface topography for zirconia alumina abrasive belt. Int J Adv Manuf Technol 89(9–12):3059–3069

    Article  Google Scholar 

  20. Fernandes LM, Lopes JC, Ribeiro FSF, Gallo R, Razuk HC, Sanchez LEA, Aguiar PR, Mello HJ, Bianchi EC (2019) Thermal model for surface grinding application. Int J Adv Manuf Technol 104(5–8):2783–2793

    Article  Google Scholar 

  21. Sato BK, Rodriguez RL, Talon AG, Lopes JC, Mello HJ, Aguiar PR, Bianchi EC (2019) Grinding performance of AISI D6 steel using CBN wheel vitrified and resinoid bonded. Int J Adv Manuf Technol 105(5–6):2167–2182

    Article  Google Scholar 

  22. Lopes JC, Fragoso KM, Garcia MV, Ribeiro FSF, Francelin AP, Sanchez LEA, Rodrigues AR, Mello HJ, Aguiar PR, Bianchi EC (2019) Behavior of hardened steel grinding using MQL under cold air and MQL CBN wheel cleaning. Int J Adv Manuf Technol 105(5–8):4373–4387

    Google Scholar 

  23. Gao KY, Chen HB, Zhang XQ, Ren XK, Chen JQ, Chen XQ (2019) A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718. Int J Adv Manuf Technol 105(1–4):217–232

    Article  Google Scholar 

  24. 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 Technol 260:9–19

    Article  Google Scholar 

  25. Chen JQ, Chen HB, Xu JJ, Wang JW, Zhang XQ, Chen XQ (2018) Acoustic signal-based tool condition monitoring in belt grinding of nickel-based superalloys using RF classifier and MLR algorithm. Int J Adv Manuf Technol 98(1–4):859–872

    Article  Google Scholar 

  26. Zhang SY, Zhou K, Ding HH, Guo J, Liu QY, Wang WJ (2018) Effects of grinding passes and direction on material removal behaviours in the rail grinding process. Materials 11(11):2293

    Article  Google Scholar 

  27. Shang W, Wang WJ, Guo J (2016) Simulation of single-grind grinding of rail grinding based on SPH method. Dia & Abra Eng 36(3):54–59

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the financial support from the Fundamental Research Funds for the Central Universities (Grant No. 2019JBM050).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenxi Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, W., Wang, J., Cheng, J. et al. Dynamic contact modeling considering local material deformation by grit indentation for abrasive belt rail grinding. Int J Adv Manuf Technol 108, 2165–2176 (2020). https://doi.org/10.1007/s00170-020-05553-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05553-x

Keywords

Navigation