Skip to main content
Log in

Numerical generation of grinding wheel surfaces based on time series method

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

Abstract

Surface integrity has a significant influence on the functional behavior of engineering components. An effective grinding wheel topography model is the foundation to realize the controllable design and manufacturing of grinding workpiece surface integrity. In this study, a moving average model (MA) based on time series method was proposed to generate the grinding wheel surface topography. 3D grinding wheel topographies were measured by an LSM 700 laser scanning confocal microscope (CLSM). The Johnson transformation system was applied to generate the non-Gaussian sequence with predetermined statistical parameters. To address the convergence and memory requirement problems, the non-linear conjugate gradient method (NCGM) with an exact line search by the secant method was employed to solve the system of nonlinear equations. The results showed a good agreement between the measured and the generated surfaces in terms of the autocorrelation function (ACF) and statistical parameters was achieved. It is worth noting that the computing time of the model developed in this study is only determined by the autocorrelation lengths which are almost independent of the size of the grinding wheel. This technique could be more efficient when applied to grinding wheels with large size. Therefore, this method may serve as an effective way to solve the grinding wheel topography reconstruction problem.

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.

Similar content being viewed by others

References

  1. Pawlus P, Graboń W, Reizer R, Górka S (2015) A study of variations of areal parameters on machined surfaces. Surf Topogr: Metrol Prop 3(2):1–8

    Google Scholar 

  2. Malkin S, Guo C (2008) Grinding technology: theory and application of machining with abrasives. Industrial Press Inc., New York

    Google Scholar 

  3. Yao Z, Gu W, Li K (2012) Relationship between surface roughness and subsurface crack depth during grinding of optical glass BK7. J Mater Process Technol 212(4):969–976

    Article  Google Scholar 

  4. Davim JP (2010) Surface integrity in machining. Springer Verlag, London

    Book  Google Scholar 

  5. Uhlmann E, Koprowski S, Weingaertner WL et al (2016) Modeling and simulation of grinding processes with mounted points: part I of II—grinding tool surface characterization. Procedia CIRP 46:599–602

    Article  Google Scholar 

  6. Stout K, Sullivan P, Dong W et al (2000) The development of methods for the characterization of roughness on three dimensions. Penton Press, London

    Google Scholar 

  7. Doman DA, Warkentin A, Bauer R (2006) A survey of recent grinding wheel topography models. Int J Mach Tools Manuf 46(3–4):343–352

    Article  Google Scholar 

  8. Hou ZB, Komanduri R (2003) On the mechanics of the grinding process—part I. Stochastic nature of the grinding process. Int J Mach Tools Manuf 43(15):1579–1593

    Article  Google Scholar 

  9. Qiao G, Dong G, Zhou M (2013) Simulation and assessment of diamond mill grinding wheel topography. Int J Adv Manuf Technol 68(9-12):2085–2093

    Article  Google Scholar 

  10. Li H, Yu T, Zhu L et al (2015) Modeling and simulation of grinding wheel by discrete element method and experimental validation. Int J Adv Manuf Technol 81(9-12):1921–1938

    Article  Google Scholar 

  11. Nguyen TA, Butler DL (2005) Simulation of precision grinding process, part 1: generation of the grinding wheel surface. Int J Mach Tools Manuf 45(11):1321–1328

    Article  Google Scholar 

  12. Salisbury EJ, Domala KV, Moon KS et al (2001) A three-dimensional model for the surface texture in surface grinding part 1: surface generation model. J Manuf Sci Eng 123(4):576–581

    Article  Google Scholar 

  13. Thomas TR (1999) Rough surfaces. Imperial College Press, London

    Google Scholar 

  14. Patir N (1978) A numerical procedure for random generation of rough surfaces. Wear 47(2):263–277

    Article  Google Scholar 

  15. Elderton WP, Johnson NL (1969) Systems of frequency curves, vol 216. Cambridge University Press, London

    Book  MATH  Google Scholar 

  16. Whitehouse DJ, Archard JF (1970) The properties of random surfaces of significance in their contact. Proc R Soc Lond 316(1524):97–121

    Article  Google Scholar 

  17. Michailidis A, Bakolas V (1999) Numerical simulation of real 3-D rough surfaces. J Balk Tribol Assoc 5(8):247–255

    Google Scholar 

  18. Bakolas V (2003) Numerical generation of arbitrarily oriented non-Gaussian three-dimensional rough surfaces. Wear 254(5-6):546–554

    Article  Google Scholar 

  19. Shewchuk JR (1994) An introduction to the conjugate gradient method without the agonizing pain. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  20. Hill I, Hill R, Holder R (1976) Algorithm AS 99: Fitting Johnson curves by moments. Appl Stat 25(2):180–189

    Article  Google Scholar 

  21. Francisco A, Brunetière N (2016) A hybrid method for fast and efficient rough surface generation. Proc IMechE Part J: J Eng Tribol 230(7):747–768

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wen Shao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, D., Shao, W., Tang, J. et al. Numerical generation of grinding wheel surfaces based on time series method. Int J Adv Manuf Technol 94, 561–569 (2018). https://doi.org/10.1007/s00170-017-0868-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-0868-y

Keywords

Navigation