Skip to main content
SpringerLink
Log in

Prediction research for surface topography of internal grinding based on mechanism and data model

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

Abstract

Grinding surface topography is an important evaluation index of grinding surface integrity. In order to realize the prediction of the grinding surface topography, a three-dimensional theoretical model of the surface topography of internal grinding was established in terms of the mutual movement between the grinding wheel and the workpiece and a data model for the surface roughness of internal grinding was set up based on the grey wolf algorithm-support vector machine (GWO-SVM). Taking the inner ring of rolling bearing as an example, the grinding experiment was carried out to analyze the influence of technological parameters on the surface topography and surface roughness. Three traditional data models were compared with the GWO-SVM data model, the comparison results showed that the GWO-SVM data model had a small change in the relative error of the predicted value of the test sample, the maximum relative error was 5.42%, and the average relative error was 3.95%. Experimental results were compared with the results of theoretical model and data model, the results show that the error of both the theoretical model and the data model is within 10%. This work should be helpful in reflecting the formation mechanism of the surface topography of the internal grinding and provide theoretical and technical support for high-precision and high-efficiency manufacturing of high-end rolling bearing.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

We confirm that data is open and transparent.

Abbreviations

M :

A grinding wheel with particle size

d gmax :

The maximum grinding particle diameter (mm)

d gavg :

The average diameter of the abrasive particles (mm)

μ 0 :

The mean value of normal distribution

σ 0 :

The variance of normal distribution

h i :

Protruding height of abrasive particles (mm)

Δ:

The average distance between adjacent grinding grains on the grinding wheel surface (mm)

S :

The structural parameter of the grinding wheel

V g :

The volume percentage of grinding grains of the grinding wheel (%)

N :

The number of grinding grains on a circle of the grinding wheel

r s :

The radius of the grinding wheel (mm)

k :

The percentage ratio of grinding grain to surface height of grinding wheel

H i :

The protruding height of the ith abrasive particle (mm)

r i :

The distance between the ith grinding grain and the center of the grinding wheel (mm)

r ω :

The radius of workpiece at the grinding point (mm)

t 0 :

The time of mutual movement(s)

θ S :

The rotation angle of a single abrasive particle of the grinding wheel (°)

θ ω :

The rotation angle of the workpiece (°)

ϕ ω :

The angular coordinates differ (°)

g mn :

Topological matrix of internal grinding surface

i :

The circumferential position of the grinding wheel

j :

The axial position of the grinding wheel

h(i,j):

The height of the grinding grain at the circumferential and axial positions of the grinding wheel(mm)

N i :

The number of peaks and valleys

Y pi :

The peak height (mm)

Y vi :

The peak valley depth (mm)

ω T :

A vector

L(y i, y Ra):

The loss function

μ :

The penalty parameter of SVM

ε :

The insensitive loss factor of SVM

\( {\xi}_i,{\xi}_i^{\ast } \) :

The slack variable

λ, λ ∗ :

The Lagrangian multipliers

K(x i, x j):

The kernel function

σ :

The parameter of kernel function

t :

The number of iterations

X i :

The position of the grey wolf

d i :

The position between the grey wolf and prey

t max :

The maximum number of iterations

n w :

Workpiece speed (r/min)

v S :

Grinding wheel speed (m/s)

n r :

Feed speed mm/min)

References

  1. Zheng W, Zhou M, Zhou L (2017) Influence of process parameters on surface topography in ultrasonic vibration-assisted end grinding of SiCp/Al composites. Int J Adv Manuf Technol 91(5–8):2347–2358

    Article  Google Scholar 

  2. Mao C, Zhou F, Hu Y, Cai P, Jiang Y, Bi Z, Peng G (2019) Tribological behavior of CBN-WC-10Co composites for dry reciprocating sliding wear. Ceram Int 45(5):6447–6458

    Article  Google Scholar 

  3. Yi J, Jin T, Zhou W, Deng ZH (2020) Theoretical and experimental analysis of temperature distribution during full tooth groove form grinding. J Manuf Process 58:101–115

    Article  Google Scholar 

  4. Chen HF, Tang JY, Deng ZH, Zhou WH (2018) Modeling and predicting surface topography of the ultrasonic assisted grinding process considering ploughing action. J Mech Eng 54(21):231–240

    Article  Google Scholar 

  5. Mao C, Sun XL, Huang H, Kang CW, Zhang MJ, Wu YQ (2016) Characteristics and removal mechanism in laser cutting of CBN-WC-10Co composites. J Mater Process Technol 230:42–49

    Article  Google Scholar 

  6. Chen J, Fang Q, Li P (2015) Effect of grinding wheel spindle vibration on surface roughness and subsurface damage in brittle material grinding. Int J Mach Tools Manuf 91:12–23

    Article  Google Scholar 

  7. Chen H, Tang J, Zhou W, Chen C (2017) The equal theoretical surface roughness grinding method for gear generating grinding. Int J Adv Manuf Technol 90(9–12):3137–3146

    Google Scholar 

  8. Hecker RL, Ramoneda IM, Liang SY (2003) Analysis of wheel topography and grit force for grinding process modeling. 5(1):13–23

  9. Deng ZH, Zhao XY, Liu W, Wan L (2016) Research of grinding wheel modeling and measuring based on the spherical coordinate and optical density. J Mech Eng 52(21):190–197

    Article  Google Scholar 

  10. Cooper WL, Lavine AS (2000) Grinding process size effect and kinematics numerical analysis. J Manuf Sci Eng 122(1):59–69

    Article  Google Scholar 

  11. Salisbury EJ, Domala KV, Moon KS, Miller MH, Sutherland JW (2001) A three-dimensional model for the surface texture in surface grinding, part 2: grinding wheel surface texture model. Journal of Manufacturing ence and Engineering 123(4):582–590

    Article  Google Scholar 

  12. Ding W, Dai C, Yu T, Xu J, Fu Y (2017) Grinding performance of textured monolayer CBN wheels: undeformed chip thickness nonuniformity modeling and ground surface topography prediction. Int J Mach Tools Manuf 122:66–80

    Article  Google Scholar 

  13. Zhou X, Xi F (2002) Modeling and predicting surface roughness of the grinding process. Int J Mach Tools Manuf 42(8):969–977

    Article  Google Scholar 

  14. Chakrabarti S, Paul S (2008) Numerical modelling of surface topography in superabrasive grinding. Int J Adv Manuf Technol 39(1–2):29–38

    Article  Google Scholar 

  15. Li C, Wu Y, Li X, Ma L, Huang H (2019) Deformation characteristics and surface generation modelling of crack-free grinding of GGG single crystals. J Mater Process Technol 279:116577

    Article  Google Scholar 

  16. Jiang JL, Ge P, Bi W, Zhang L, Wang D, Zhang Y (2013) 2D/3D ground surface topography modeling considering dressing and wear effects in grinding process. International Journal of Machine Tools and Manufacture 74:29–40

    Article  Google Scholar 

  17. Jamshidi H, Gurtan M, Budak E (2019) Identification of active number of grits and its effects on mechanics and dynamics of abrasive processes. J Mater Process Technol 273:116239

    Article  Google Scholar 

  18. Jiang JL, Ge PQ, Hong J (2013) Study on micro-interacting mechanism modeling in grinding process and ground surface roughness prediction. Int J Adv Manuf Technol 67(5):1035–1052

    Article  Google Scholar 

  19. Kara F, Karabatak M, Ayyıldız M, Nas E (2020) Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting. Journal of Materials Research and Technology 9(1):969–983

    Article  Google Scholar 

  20. Kara F (2018) Optimization of surface roughness in finish milling of AISI P20+S plastic mold steel. Materials and Technology 52(2):195–200

    Google Scholar 

  21. Kara F, Takmaz A (2019) Optimization by the Taguchi method of effect on the surface roughness of cryogenic treatment applied to cutting tools. Material Testing 61(11):1101–1104

    Article  Google Scholar 

  22. Fredj NB, Amamou R (2006) Ground surface roughness prediction based upon experimental design and neural network models. International Journal Advanced Manufacturing Technology 31(1–2):24–36

    Article  Google Scholar 

  23. Alao A, Konneh M (2011) Surface finish prediction models for precision grinding of silicon. Int J Adv Manuf Technol 58(9–12):949–967

    Google Scholar 

  24. Gopan V, Wins KLD, Surendran A (2018) Integrated ANN-GA approach for predictive modeling and optimization of grinding parameters with surface roughness as the response. Materials today: proceedings 5(5):12133–12141

    Google Scholar 

  25. Lipiński D, Bałasz B, Rypina Ł (2018) Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation. International Journal Advanced Manu-facturing Technology 94:1335–1347

    Article  Google Scholar 

  26. Liu W, Deng ZH, Wang L, Wu Q (2014) Parameter optimization on precision grinding of ceramic sphere using experiment and genetic neural network. China Mechanical Engineering 25(4):451–455

    Google Scholar 

  27. Jing J, Feng P, Wei S, Zhao H (2017) Investigation on surface morphology model of Si3N4 ceramics for rotary ultrasonic grinding machining based on the neural network. Appl Surf Sci 396:85–94

    Article  Google Scholar 

  28. Nguyen D, Yin S, Tang Q, Son X, Duc L (2019) Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precis Eng 18:275–292

    Article  Google Scholar 

  29. Kara F, Iek A, Demir H (2013) Multiple regression and ANN models for surface quality of cryogenically-treated AISI 52100 bearing steel. Journal of the Balkan Tribological Association 19(4):570–584

    Google Scholar 

  30. Malkin S (1989) Grinding technology: theory and applications of machining with abrasives. Horwood, Society of Manufacturing Engineers, pp 43–46

    Google Scholar 

  31. Lv LS, Deng ZH, Liu T, Li ZY, Liu W (2020) Intelligent technology in grinding process driven by data: a review. J Manuf Process 58:1039–1051

    Article  Google Scholar 

  32. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46–61

    Article  Google Scholar 

  33. Kulkarni O, Kulkarni S (2018) Process parameter optimization in WEDM by grey wolf optimizer. Materials today: Proceedings 5(2):4402–4412

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (grant number U1809221), high-tech Industry of Science and Technology Innovation Leading Project of Hunan Province of China (grant number 2020GK003), and the Special project of National Independent Innovation Demonstration Zone of Changzhutan (grant number 2GK017XK2302).

Author information

Authors and Affiliations

  1. Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult to Machine Material, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Qianwei Gu, Zhaohui Deng, Lishu Lv, Tao Liu & Hongzhao Teng

  2. Intelligent Manufacturing Institute, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Qianwei Gu, Zhaohui Deng, Lishu Lv, Tao Liu & Hongzhao Teng

  3. Ultra-precision Machining Center, College of Mechanical Engineering, Zhejiang University of Technology, Zhejiang, 310023, China

    Dongfeng Wang & Julong Yuan

  4. Luoyang Bearing Science & Technology Co, Ltd, Henan, 471000, China

    Dongfeng Wang

Authors
  1. Qianwei Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaohui Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lishu Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongzhao Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dongfeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julong Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qianwei Gu: Investigation, data curation, writing of original draft, and editing.

Zhaohui Deng: Reviewing, editing, and funding acquisition.

Lishu Lv: Investigation, supervision, writing, reviewing, and editing.

Tao Liu: Supervision, writing, reviewing, and editing.

Hongzhao Teng: Reviewing, and editing.

Dongfeng Wang: Supervision, project administration.

Julong Yuan: Project administration, supervision, and reviewing.

Corresponding author

Correspondence to Zhaohui Deng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

The codes generated during the current study are available from the corresponding author on reasonable request.

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

Gu, Q., Deng, Z., Lv, L. et al. Prediction research for surface topography of internal grinding based on mechanism and data model. Int J Adv Manuf Technol 113, 821–836 (2021). https://doi.org/10.1007/s00170-021-06604-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-06604-7

Keywords

Search

Navigation