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On energetic evaluation of robotic belt grinding mechanisms based on single spherical abrasive grain model

  • Zeyuan Yang
  • Xiaohu Xu
  • Dahu ZhuEmail author
  • Sijie YanEmail author
  • Han Ding
ORIGINAL ARTICLE
  • 69 Downloads

Abstract

A tentative study from the perspective of abrasive grain geometry in this paper is conducted to investigate the specific energy and energy efficiency for clarifying the robotic belt grinding mechanisms. The energy efficiency model is established based on the friction coefficient model of the single spherical grain, then the experiments and simulation are implemented to energetically evaluate the microscale material removal mechanisms from the specific energy contributions. It has been demonstrated that the specific plowing energy is more predominant than both the specific cutting and sliding energy in robotic belt grinding, resulting in the energy efficiency ranges between 17 and 41 %. Both the large grain size and normal contact force can be taken as optimization strategies to maximize the energy efficiency for material removal.

Keywords

Robotic belt grinding Specific energy Energy efficiency Grain size Friction coefficient 

Notes

Funding information

This study is financially supported by the National Nature Science Foundation of China (No. 51675394), the National Key Research and Development Program of China (No. 2017YFB1303403), the State Key Laboratory of Digital Manufacturing Equipment and Technology (No.DMETKF2018018), and the Graduates’ Innovation Fund, Huazhong University of Science and Technology (No. 2019ygscxcy012).

References

  1. 1.
    Wang T, Tao Y, Liu H (2018) Current researches and future development trend of intelligent robot: a review. Int J Autom Comput 15(5):525–546CrossRefGoogle Scholar
  2. 2.
    Xu X, Zhu D, Wang J, Yan S, Ding H (2018) Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere. Robot Comput Integr Manuf 51:189–201CrossRefGoogle Scholar
  3. 3.
    Li W, Xie H, Zhang G, Yan S, Yin Z (2016) 3-D shape matching of a blade surface in robotic grinding applications. IEEE-ASME Trans Mechatronics 21(5):2294–2306CrossRefGoogle Scholar
  4. 4.
    Xiao G, Huang Y, Wang J (2017) Path planning method for longitudinal micromarks on blisk root-fillet with belt grinding. Int J Adv Manuf Technol 95(1–4):797–810Google Scholar
  5. 5.
    Zhang T, Su J (2018) Collision-free planning algorithm of motion path for the robot belt grinding system. Int J Adv Robot Syst 15(4):172988141879377MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang J, Liu G, Zang X, Li L (2016) A hybrid passive/active force control scheme for robotic belt grinding system. IEEE International Conference on Mechatronics & Automation, pp 737–742Google Scholar
  7. 7.
    Chen F, Zhao H, Li D, Chen L, Tan C, Ding H (2018) Robotic grinding of a blisk with two degrees of freedom contact force control. Int J Adv Manuf Technol 101(1–4):461–474Google Scholar
  8. 8.
    Yan S, Xu X, Yang Z, Zhu D, Ding H (2019) An improved robotic abrasive belt grinding force model considering the effects of cut-in and cut-off. J Manuf Process 37:496–508CrossRefGoogle Scholar
  9. 9.
    Wang Y, Huang Y, Chen Y, Yang Z (2016) Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Technol 82(9–12):2113–2122CrossRefGoogle Scholar
  10. 10.
    Xu X, Zhu D, Zhang H, Yan S, Ding H (2019) Application of novel force control strategies to enhance robotic abrasive belt grinding quality of aero-engine blades. Chin J Aeronaut.  https://doi.org/10.1016/j.cja.2019.01.023
  11. 11.
    Zhu D, Xu X, Yang Z, Zhuang K, Yan S, Ding H (2018) Analysis and assessment of robotic belt grinding mechanisms by force modeling and force control experiments. Tribol Int 120:93–98CrossRefGoogle Scholar
  12. 12.
    Lv H, Song Y, Jia P, Gan Z, Qi L (2010) An adaptive modeling approach based on ESN for robotic belt grinding. The 2010 IEEE International Conference on Information and Automation, pp 787–792Google Scholar
  13. 13.
    Song Y, Liang W, Yang Y (2012) A method for grinding removal control of a robot belt grinding system. J Intell Manuf 23(5):1903–1913CrossRefGoogle Scholar
  14. 14.
    Wang P, Gao R, Yan R (2017) A deep learning-based approach to material removal rate prediction in polishing. CIRP Ann Manuf Technol 66(1):429–432CrossRefGoogle Scholar
  15. 15.
    Kannappan S, Malkin S (1972) Effects of grain size and operating parameters on the mechanics of grinding. J Eng Ind 94(3):833–842CrossRefGoogle Scholar
  16. 16.
    Hahn RS (1966) On the mechanics of the grinding process under plunge cut conditions. J Eng Ind 88(1):72–79CrossRefGoogle Scholar
  17. 17.
    Jourani A, Hagege B, Bouvier S, Bigerelle M, Zahouani H (2013) Influence of abrasive grain geometry on friction coefficient and wear rate in belt finishing. Tribol Int 59:30–37CrossRefGoogle Scholar
  18. 18.
    Xiao G, Huang Y (2016) Equivalent self-adaptive belt grinding for the real-R edge of an aero-engine precision-forged blade. Int J Adv Manuf Technol 83(9–12):1697–1706CrossRefGoogle Scholar
  19. 19.
    Wang W, Liu F, Liu Z, Yun C (2017) Prediction of depth of cut for robotic belt grinding. Int J Adv Manuf Technol 91(1–4):699–708CrossRefGoogle Scholar
  20. 20.
    Zhu D, Luo S, Yang L, Chen W, Yan S, Ding H (2015) On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys. Tribol Int 90:55–59CrossRefGoogle Scholar
  21. 21.
    Ghosh S, Chattopadhyay AB, Paul S (2008) Modelling of specific energy requirement during high-efficiency deep grinding. Int J Mach Tool Manu 48(11):1242–1253CrossRefGoogle Scholar
  22. 22.
    Latifinavid M, Ei K (2017) Hybrid model based on energy and experimental methods for parallel hexapod-robotic light abrasive grinding operations. Int J Adv Manuf Technol 93(9–12):3873–3887CrossRefGoogle Scholar
  23. 23.
    Ren Y, Zhang B, Zhou Z (2009) Specific energy in grinding of tungsten carbides of various grain sizes. CIRP Ann Manuf Technol 58(1):299–302CrossRefGoogle Scholar
  24. 24.
    Zhang T, Jiang F, Yan L, Xu X (2018) Research on the size effect of specific cutting energy based on numerical simulation of single git scratching. J Manuf Sci Eng Trans ASME 140(7):071017CrossRefGoogle Scholar
  25. 25.
    Marinescu ID, Rowe WB, Dimitrov B, Inasaki I (2004) Tribology of abrasive machining processes. William Andrew, Inc, New YorkGoogle Scholar
  26. 26.
    Goddard J, Wilman H (1962) A theory of friction and wear during the abrasion of metals. Wear 5(2):114–135CrossRefGoogle Scholar
  27. 27.
    Xu X, Zhu D, Zhang H, Yan S, Ding H (2017) TCP-based calibration in robot-assisted belt grinding of aero-engine blades using scanner measurements. Int J Adv Manuf Technol 90(1–4):635–647CrossRefGoogle Scholar
  28. 28.
    Wei W, Chao Y, Ling Z, Gao Z (2011) Designing and optimization of an off-line programming system for robotic belt grinding process. Chin J Mech Eng 24(04):647–655CrossRefGoogle Scholar
  29. 29.
    Ma J, Ge X, Chang SI, Lei S (2014) Assessment of cutting energy consumption and energy efficiency in machining of 4140 steel. Int J Adv Manuf Technol 74(9–12):1701–1708CrossRefGoogle Scholar
  30. 30.
    Wu H, Huang Y, Huang Z, Cheng G (2011) Experimental research on the abrasive belt grinding turbine blades material 1Cr13 stainless steel. Key Eng Mater 487:452–456CrossRefGoogle Scholar
  31. 31.
    Zhang J, Li H, Zhang M, Yan Z, Wang L (2016) Study on force modeling considering size effect in ultrasonic-assisted micro-end grinding of silica glass and Al2O3 ceramic. Int J Adv Manuf Technol 89(1–4):1173–1192Google Scholar
  32. 32.
    Khellouki A, Rech J, Zahouani H (2013) Energetic analysis of cutting mechanisms in belt finishing of hard materials. Proc Inst Mech Eng B J Eng Manuf 227(9):1409–1413CrossRefGoogle Scholar
  33. 33.
    Demir H, Gullu A, Ciftci I, Seker U (2010) An investigation into the influences of grain size and grinding parameters on surface roughness and grinding forces when grinding. J Mech Eng 56(7–8):447–454Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.State Key Laboratory of Digital Manufacturing Equipment and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Advanced Technology for Automotive ComponentsWuhan University of TechnologyWuhanChina
  3. 3.Hubei Collaborative Innovation Center for Automotive Components TechnologyWuhan University of TechnologyWuhanChina

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