Skip to main content
SpringerLink
Log in

The influence of wear volume on surface quality in grinding process based on wear prediction model

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

Abstract

During the grinding process, the workpiece is not only cut by abrasive grains but adhesive wear also occurs due to high temperature and heavy load, reducing the surface quality of the workpiece. In this paper, a wear test method considering speed, force, wear coefficient, temperature and hardness was proposed. A new physical model of wear prediction was established based on the finite element method and numerical simulation technology. The wear test was carried out on a grinding machine. Comprehensive research on the relationship between the force, temperature, surface morphology and wear volume of the grinding process was studied. The relationship between workpiece speed, grinding depth, cooling lubrication conditions and wear volume of the grinding process was studied. The results show that the wear model can achieve numerical prediction and trend prediction of grinding temperature, surface profile and wear volume, with relative errors between the theoretical and actual values of wear and grinding temperature of 9.84% and 2.07%, respectively. This study provides support for wear prediction and surface quality control of the grinding process from the perspective of temperature and micro material removal.

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

References

  1. Naskar A, Choudhary A, Paul S (2020) Wear mechanism in high-speed superabrasive grinding of titanium alloy and its effect on surface integrity. Wear 462–463:203475. https://doi.org/10.1016/j.wear.2020.203475

  2. Kahraman MF, Öztürk S (2019) Experimental study of newly structural design grinding wheel considering response surface optimization and Monte Carlo simulation. Measurement 147:106825. https://doi.org/10.1016/j.measurement.2019.07.053

    Article  Google Scholar 

  3. Aydin G, Karakurt I, Aydiner K (2013) Wear performance of saw blades in processing of granitic rocks and development of models for wear estimation. Rock Mech Rock Eng 46:1559–1575. https://doi.org/10.1007/s00603-013-0382-y

    Article  Google Scholar 

  4. Yu TY, Bastawros AF, Chandra A (2017) Experimental and modeling characterization of wear and life expectancy of electroplated CBN grinding wheels. Int J Mach Tools Manuf 121:70–80. https://doi.org/10.1016/j.ijmachtools.2017.04.013

    Article  Google Scholar 

  5. Dai CW, Ding WF, Xu JH, Fu YC, Yu TY (2017) Influence of grain wear on material removal behavior during grinding nickel-based superalloy with a single diamond grain. Int J Mach Tools Manuf 113:49–58. https://doi.org/10.1016/j.ijmachtools.2016.12.001

    Article  Google Scholar 

  6. Li BK, Yin JF, Yin J, Zhu YJ, Zhang X, Ding WF (2020) Grain wear evolution of cubic boron nitride abrasives during single grain grinding of powder metallurgy superalloy FGH96. Ceram Int 47:2508–2516. https://doi.org/10.1016/j.ceramint.2020.09.094

    Article  Google Scholar 

  7. Jiang JL, Ge PQ, Bi WB, Zhang L, Wang DX, Zhang Y (2013) 2D/3D ground surface topography modeling considering dressing and wear effects in grinding process. Int J Mach Tools Manuf 74:29–40. https://doi.org/10.1016/j.ijmachtools.2013.07.002

    Article  Google Scholar 

  8. HtunHtun Oo, Wang W, Liu ZH (2020) Tool wear monitoring system in belt grinding based on image-processing techniques. Int J Adv Manuf Technol 111(7–8):2215–2229. https://doi.org/10.1007/s00170-020-06254-1

    Article  Google Scholar 

  9. Sieniawski J, Nadolny K (2018) Experimental study into the grinding force in surface grinding of steel CrV12 utilizing a zonal centrifugal coolant provision system. Proc Ins Mech Eng, Part B: J Eng Manufact 232:394–403. https://doi.org/10.1177/0954405416645256

    Article  Google Scholar 

  10. Li Z, Ding WF, Ma CY, Xu JH (2017) Grinding temperature and wheel wear of porous metal-bonded cubic boron nitride superabrasive wheels in high-efficiency deep grinding. Proc Ins Mech Eng, Part B: J Eng Manufact 231:1961–1971. https://doi.org/10.1177/0954405415617928

    Article  Google Scholar 

  11. Curtis D, Krain H, Winder A, Novovic D (2021) Impact of grinding wheel specification on surface integrity and residual stress when grinding Inconel 718. Proc Ins Mech Eng, Part B: J Eng Manufact 235:1668–1681. https://doi.org/10.1177/0954405420961209

    Article  Google Scholar 

  12. Li QL, Xiu SC, Yao YL, Sun C, Zheng SG (2020) Study on surface material removal uniformity in double side grinding based on grain trajectories. Int J Adv Manuf Technol 107:2865–2873. https://doi.org/10.1007/s00170-020-05147-7

    Article  Google Scholar 

  13. Qu C, Lv YJ, Yang ZY, Xu XH, Zhu DH, Yan SJ (2019) An improved chip-thickness model for surface roughness prediction in robotic belt grinding considering the elastic state at contact wheel-workpiece interface. Int J Adv Manuf Technol 104:3209–3217. https://doi.org/10.1007/s00170-019-04332-7

    Article  Google Scholar 

  14. Xi XX, Yu T, Ding W F, Xu JH (2018) Grinding of Ti 2 AlNb intermetallics using silicon carbide and alumina abrasive wheels: tool surface topology effect on grinding force and ground surface quality. Precis Eng S014163591830031X. https://doi.org/10.1016/j.precisioneng.2018.03.007

  15. Miao Q, Ding W, Kuang W, Yang C (2019) Grinding force and surface quality in creep feed profile grinding of turbine blade root of nickel-based superalloy with microcrystalline alumina abrasive wheels - sciencedirect. Chin J Aeronaut 34(2):576–585. https://doi.org/10.1016/j.cja.2019.11.006

    Article  Google Scholar 

  16. Hsu SM, Shen MC, Ruff AW (1997) Wear prediction for metals. Tribol Int 30:377–383. https://doi.org/10.1016/S0301-679X(96)00067-9

    Article  Google Scholar 

  17. Zhang GL, Liu Y, Wang YC, Liu XF (2019) A friction-dissipation based method for quantity model and prediction of graphite/WC-Ni wear under dry sliding. Tribology 39: 221–227. https://doi.org/10.16078/j.tribology.2018109

  18. Ma JM, Chen J, Li J, Li QL, Ren CY (2015) Wear analysis of swash plate/slipper pair of axis piston hydraulic pump. Tribol Int 90:467–472. https://doi.org/10.1016/j.triboint.2015.05.010

    Article  Google Scholar 

  19. Ramalho A (2015) Wear modelling in rail–wheel contact. Wear 330:524–532. https://doi.org/10.1016/j.wear.2015.01.067

    Article  Google Scholar 

  20. Gao BH, Bao WC, Jin T, Chen CQ, Qu MN, Lu AG (2021) Variation of wheel-work contact geometry and temperature responses: thermal modeling of cup wheel grinding. Int J Mech Sci 196:106305. https://doi.org/10.1016/j.ijmecsci.2021.106305

    Article  Google Scholar 

  21. Wang WX, Salvatore F, Rech J, Li JY (2018) Comprehensive investigation on mechanisms of dry belt grinding on AISI52100 hardened steel. Tribol Int 121:310–320. https://doi.org/10.1016/j.triboint.2018.01.019

    Article  Google Scholar 

  22. Archard JF (1953) Contact and rubbing of flat surfaces. J Appl Phys 24:981–988. https://doi.org/10.1063/1.1721448

    Article  Google Scholar 

  23. 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 43:1579–1593. https://doi.org/10.1016/S0890-6955(03)00186-X

    Article  Google Scholar 

  24. Hecker RL, Liang SY, Wu XJ, Xia P, Jin DGW (2007) Grinding force and power modeling based on chip thickness analysis. Int J Adv Manuf Technol 33:449–459. https://doi.org/10.1007/s00170-006-0473-y

    Article  Google Scholar 

  25. Madopothula U, Lakshmanan V, Nimmagadda RB, Elango P (2017) Prediction of temperature distribution in the workpiece during multi-pass grinding by finite volume method. Int J Precis Eng Manuf 18:1485–1493. https://doi.org/10.1007/s12541-017-0176-3

    Article  Google Scholar 

  26. Kim NK, Guo C, Malkon S (1997) Heat flux distribution and energy partition in creep-feed grinding. CIRP Ann Manuf Technol 46(2):227–232. https://doi.org/10.1016/S0007-8506(07)60814-2

    Article  Google Scholar 

  27. Hahn RS (1962) On the nature of the grinding process. Proc 3rd Mach Tool Des Res Conf 126–154

  28. Li BK, Li CH, Zhang YB, Wang YG, Jia DZ, Yang M (2016) Grinding temperature and energy ratio coefficient in MQL grinding of high-temperature nickel-base alloy by using different vegetable oils as base oil. Chin J Aeronaut 29:1084–1095. https://doi.org/10.1016/j.cja.2015.10.012

    Article  Google Scholar 

  29. Lee RS, Jou JL (2003) Application of numerical simulation for wear analysis of warm forging die. J Mater Process Technol 140:43–48. https://doi.org/10.1016/S0924-0136(03)00723-4

    Article  Google Scholar 

  30. Tao WJ, Zhong Y, Feng HT, Wang YL (2013) Model for wear prediction of roller linear guides. Wear 305:260–266. https://doi.org/10.1016/j.wear.2013.01.047

    Article  Google Scholar 

  31. Silva R, Ferreira DC, Dutra F, Silva S (2021) Simultaneous real time estimation of heat flux and hot spot temperature in machining process using infrared camera. Case Stud Therm Eng 28.https://doi.org/10.1016/j.csite.2021.101352

  32. NiżankowskiStruzikiewicz C (2017) Comparative tests of the proper active grinding powers and maximum grinding temperatures, conducted on corrosion-resistant steel surfaces, using aluminium oxynitride and noble electrocorundum grinding wheels. Int J Adv Manuf Technol 89:273–282. https://doi.org/10.1007/s00170-016-9084-4

    Article  Google Scholar 

Download references

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was financially supported by the National Natural Science Foundation of China (No.52175113, No.51905406), the Key Laboratory Research Program of Education Department of Shaanxi Province (No.18JS044) and the International Science and Technology Cooperation and Exchange Program of Shaanxi Province (No.2020KW-014).

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering, Xi’an Technological University, 2 Xuefu Road , Xi’an Shaanxi, 710021, China

    Wei Cao, Zhao Han, Ziqi Chen, Zili Jin, Jiajun Wu & Jinxiu Qu

  2. Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design, Xi’an Shaanxi, 710001, China

    Dong Wang

Authors
  1. Wei Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhao Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zili Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiajun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jinxiu Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Modeling, simulation and paper writing were carried out by Cao Wei and Han Zhao, experiments and data processing were completed by Chen Ziqi, Jin Zili and Wu Jiajun and manuscript materials were sorted out by Qu Jinxiu and Wang Dong. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhao Han.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Highlights

1. A wear prediction physical model was proposed considering speed, contact force, temperature, wear coefficient and material hardness for the grinding process, which can predict surface morphology.

2. A method for measuring and calculating surface wear in the grinding process was proposed.

3. The grinding temperature field, wear volume, surface morphology and wear mechanism were analysed, which provides technical support for improving grinding surface quality from the perspective of grinding burn and adhesive wear.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, W., Han, Z., Chen, Z. et al. The influence of wear volume on surface quality in grinding process based on wear prediction model. Int J Adv Manuf Technol 121, 5793–5809 (2022). https://doi.org/10.1007/s00170-022-09575-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09575-5

Keywords

Search

Navigation