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Optimization of internal plunge grinding using collaboration of the air-grinding and the material removal model based on the power signal

  • Yulun ChiEmail author
  • Jiajian Gu
  • Haolin Li
ORIGINAL ARTICLE
  • 41 Downloads

Abstract

This paper introduces a new optimum method for internal plunge grinding by using the material removal model based on the power signals that were extracted from machine wheel spindle. An automatic grinding cycle optimization functional module is proposed to improve the process grinding efficiency while ensuring component quality requirements. Aiming at the productivity, the functional module encompasses a more comprehensive set of realistic constraints and the manufacturing values. By combining both the air-grinding reduction method and the grinding process optimization method, the functional module has been integrated to provide a new algorithm for analysis and optimization of bearing grinding process. In the paper, an experimental optimization example demonstrates a 33.6% reduction in production cycle time for bearing grinding process. The results confirm that this functional module can be used to save the total grinding cost greatly in industrial implication.

Keywords

Internal plunge grinding Process optimization Air-grinding Material removal model 

Notation

an

total stock removal

ap

grinding depth

b

grinding wheel width

bn

real stock removal

de

equivalent diameter of the wheel

ds

wheel diameter

dw

workpiece diameter

en

power signal deviation

Ft

grinding tangential force

J

mechanical equivalent of heat

kc

grinding force coefficient

kp

coefficient of power

knt

proportionality coefficient

ke

overall effective stiffness

L

contact length of the grinding contact zone

lA

air-grinding process

lB

size of workpiece blank irregular shape

m

proportionality coefficient

n

order of infeed stage in the grinding cycle

nw

workpiece rotational speed

P

grinding power

\( {P}_{\mathrm{lim}}^{\prime } \)

limitation of maximum grinding power

Pmax

maximum power

qlim

maximum limitation of the heat flux

Ra

surface roughness

Rm

additional residual out-of-roundness

Rn

roundness of workpiece

R0 and R

empirical constants

Rw

ratio of the heat flowing

Rnmax

maximum constrained grinding roundness

Rlim

limited surface roughness

\( {r}_n^{grind} \)

real workpiece radius reduction of the nth infeed stage

tn

the n stages infeed time

trough

roughing infeed time

tsemi

semi-finishing infeed time

tfinish

finishing infeed time

tspark

spark-out time

vs

grinding wheel speed

vw

grinding workpiece speed

τ

time constant

objective function of the shortest grinding cycle time

δn

elastic deflection

θmb

grinding temperature of the workpiece

θmax

grinding burn limit temperature

λ

coefficient of heat emission

α

temperature diffusivity of the workpiece

\( {\overset{.}{r}}_n \)

material removal rate

\( {\overset{.}{u}}_i \)

ith command infeed velocity

Notes

Funding information

This study was financially supported by the Science and Technology Commission of Shanghai Municipality (No. 17DZ2283300).

References

  1. 1.
    Gao YS, Jones B (1999) Fast time constant estimation for plunge grinding process control. Int J Mach Tools Manuf 39:143–156CrossRefGoogle Scholar
  2. 2.
    Kim HY, Kim SR, Ahn JH, Kim SH (2001) Processing monitoring of centerless grinding using acoustic emission. J Mater Process Technol 111:273–278CrossRefGoogle Scholar
  3. 3.
    Yan Y, Xu J, Wiercigroch M (2017) Regenerative chatter in a plunge grinding process with workpiece imbalance. Int J Adv Manuf Technol 89:2845–2862CrossRefGoogle Scholar
  4. 4.
    Lezanski P, Pilacinska M (2018) The dominance-based rough set approach to cylindrical plunge grinding process diagnosis. J Intell Manuf 29:989–1004CrossRefGoogle Scholar
  5. 5.
    Yu GW, Wang Q, Song ZY, Fang DS, Li YW, Yao Y (2019) Toward the temperature distribution on ball bearing inner rings during single-grit grinding. Int J Adv Manuf Technol 100:1355–1362.  https://doi.org/10.1007/s00170-018-03238-0 CrossRefGoogle Scholar
  6. 6.
    Ahrens M, Damm J, Dagen M, Denkena B, Ortmaier T (2017) Estimation of dynamic grinding wheel wear in plunge grinding. Procedia CIRP 58:422–427CrossRefGoogle Scholar
  7. 7.
    Yan Y, Xu J, Wiercigroch M (2015) Non-linear analysis and quench control of chatter in plunge grinding. Int J Non Linear Mech 70:134–144CrossRefGoogle Scholar
  8. 8.
    Denkena B, Ortmaier T, Ahrens M, Fischer R (2014) Monitoring of grinding wheel defects using recursive estimation. Int J Adv Manuf Technol 75:1005–1015CrossRefGoogle Scholar
  9. 9.
    Nguyen T, Liu M, Zhang LC, Wu Q, Sun DL (2014) Effect of cyclic heating on the hardened layer properties generated by plunge cylindrical grinding. Adv Mater Res 1017:539–543CrossRefGoogle Scholar
  10. 10.
    Lee CW (2009) A control-oriented model for the cylindrical grinding process. Int J Adv Manuf Technol 44(7-8):657–666CrossRefGoogle Scholar
  11. 11.
    Dias EA, Pereira FB, Filho SLMR, Brandao LC (2016) Monitoring of through-feed centreless grinding processes with acoustic emission signals. Measurement 94:71–79CrossRefGoogle Scholar
  12. 12.
    Badger J, Murphy S, O’Donnell GE (2018) Acoustic emission in dressing of grinding wheels: AE intensity, dressing energy, and quantification of dressing sharpness and increase in diamond wear-flat size. Int J Mach Tools Manuf 125:11–19CrossRefGoogle Scholar
  13. 13.
    Boaron A, Weingaertner WL (2018) Dynamic in-process characterization method based on acoustic emission for topograhic assessment of conventional grinding wheels. Wear 406:218–229CrossRefGoogle Scholar
  14. 14.
    Liu CS, Li YA (2018) Evaluation of grinding wheel loading phenomena by using acoustic emission signals. Int J Adv Manuf Technol 99:1109–1117CrossRefGoogle Scholar
  15. 15.
    Marsh ER, Moerlein AW, Deakyne TRS, Doren MJV (2008) In-process measurement of form error and force in cylindrical plunge grinding. Precis Eng 32:348–352CrossRefGoogle Scholar
  16. 16.
    Zhang YB, Li CH, Ji HJ, Yang YX, Yang M, Jia DZ, Zhang XP, Li RZ, Wang J (2017) Analysis of grinding mechanics and improved predictive force model based on material-removal and plastic-stacking mechanisms. Int J Mach Tools Manuf 122:81–97CrossRefGoogle Scholar
  17. 17.
    Kannan K, Arunachalam N (2018) Grinding wheel redress life estimation using force and surface texture. Procedia CIRP 72:1439–1444CrossRefGoogle Scholar
  18. 18.
    Tawakoli T (2008) Developments in grinding process monitoring and evaluation of results. Int J Mechatron Manuf Syst 1(4):307–320Google Scholar
  19. 19.
    Axinte D, Gindy N (2004) Assessment of the effectiveness of a spindle power signal for tool condition monitoring in machining processes. Int J Prod Res 42:2679–2691CrossRefGoogle Scholar
  20. 20.
    CHI YL, LI HL, Chen X (2016) In-process monitoring and analysis of bearing outer race way grinding based on the power signal. Proc IMechE B J Eng Manuf 3(1): 1–1):14Google Scholar
  21. 21.
    YL CHI, LI HL (2016) A general material removal model for multi-infeed internal plunge grinding by using power signal. J Chin Soc Mechn Eng 37(4):359–365Google Scholar
  22. 22.
    Kim SH, Ko TJ, Ahn JH (2001) Reduction of air-grinding time using collaboration of dual sensors. Int J Adv Manuf Technol 17(4):252–256CrossRefGoogle Scholar
  23. 23.
    Barrenetxea D, Alvarez J, Marquinez JI, Gallego I, Perello IM, Krajnik P (2014) Stability analysis and optimization algorithms for the set-up of infeed centerless grinding. Int J Mach Tools Manuf 84(6):17–32CrossRefGoogle Scholar
  24. 24.
    Jayakmar T, Mukhopadyay CK, Venugpal S, Mannan SL, Raj B (2005) A review of the application of acoustic emission techniques for monitoring forming and grinding processes. J Mater Process Technol 159:48–61CrossRefGoogle Scholar
  25. 25.
    Li GF, Wang LS, Yang LB (2002) Multi-parameter optimization and control of the cylindrical grinding process. J Mater Process Technol 129(1–3):232–236CrossRefGoogle Scholar
  26. 26.
    Malkin, S (2002) Theory and application of machining with abrasives, grinding technology. Northeastern University Press, pp: 130 - 153Google Scholar
  27. 27.
    Chen X, Allanson D, Thomas A, Moruzzi JL, Rowe WB (1994) Simulation of feed cycles for grinding between centers. Int J Mach Tools Manuf 34(5):603–616CrossRefGoogle Scholar
  28. 28.
    Xiao G, Malkin S (1996) On-line optimization for internal plunge grinding. CIRP Ann Manuf Technol 45(1):287–292CrossRefGoogle Scholar
  29. 29.
    Choi TJ, Subrahmanya N, Li H, Shin YC (2008) Generalized practical models of cylindrical plunge grinding processes. Int J Mach Tools Manuf 48(1):61–72CrossRefGoogle Scholar
  30. 30.
    Zhou H, Ding WF, Liu CD (2019) Analytical modelling of temperature in cylindrical grinding to predict grinding burns. Int J Adv Manuf Technol 20:13–25Google Scholar
  31. 31.
    Malkin S, Koren Y (1984) Optimal infeed control for accelerated spark-out in plunge grinding. ASME J Ind 106(1):70–74CrossRefGoogle Scholar
  32. 32.
    Jiang C, Song Q, Guo D, Li HL (2014) Estimation algorithm of minimum dwell time in precision cylindrical plunge grinding using acoustic emission signal. Int J Precis Eng Manuf 15(4):601–607CrossRefGoogle Scholar
  33. 33.
    Fu YC, Xu JH, Xu HJ (2002) Optimization design of grinding wheel topography for high efficiency grinding. J Mater Process Technol 129:118–122CrossRefGoogle Scholar
  34. 34.
    Moerlein AW (2009) In-process force measurement for diameter control in presion cylindrical grinding. Int J Adv Manuf Technol 42(1):93–101CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Mechanical EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina

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