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Time-varying analytical model of ball-end milling tool wear in surface milling

  • Zemin Zhao
  • Xianli LiuEmail author
  • Caixu Yue
  • Hongyan Zhang
  • Rongyi Li
  • Steven Y. Liang
ORIGINAL ARTICLE

Abstract

The quality of a workpiece depends on the time-varying characteristics of the tool performance. Tool wear is an important factor that affects the serviceability of a tool. However, most existing models do not fully contain the tool design and processing parameters and do not focus on the time-varying characteristics of wear. In this paper, the wear distribution characteristics of a ball-end mill were investigated by analysing the position function in the milling life model, and the geometric model of the wear volume was proposed based on the principle of calculus. By geometric and physical modelling, the tool design and machining parameters were completely introduced in the model, and the time variable is introduced when solving the wear rate. After the models and assumptions are validated, the factors affecting wear are cross-analysed and discussed through simulation. Wear models including time variables, tool design, and milling parameters are obtained, which can be used directly for tool design and analysis. This research provides a basis for the tool analytical design.

Keywords

Ball-end milling tool Surface milling Time-varying analytical model of wear modelling Wear 

Notes

Supplementary material

170_2019_4783_MOESM1_ESM.docx (26 kb)
ESM 1 (DOCX 25 kb)
170_2019_4783_MOESM2_ESM.docx (20 kb)
ESM 2 (DOCX 19 kb)

References

  1. 1.
    Abele E, Hasenfratz C, Bücker M (2017) Modeling of process forces with respect to technology parameters and tool wear in milling Ti6Al4V. Prod Eng 11(3):285–294CrossRefGoogle Scholar
  2. 2.
    Yue C, Gao H, Liu X, Liang SY (2018) Part functionality alterations induced by changes of surface integrity in metal milling process: a review. Appl Sci 8:2550CrossRefGoogle Scholar
  3. 3.
    Eshi U (1982) Cutting and grinding processing. Mechanical Industry Press, ChinaGoogle Scholar
  4. 4.
    Ning L, Veldihuis SC (2006) Mechanistic modeling of ball end milling including tool wear. J Manuf Process 8(1):21–28CrossRefGoogle Scholar
  5. 5.
    Zhang C, Zhou L, An L (2008) Modeling and error compensation for tool wear of ball-end milling cutter. Aust J Mech Eng 44(2):207–212CrossRefGoogle Scholar
  6. 6.
    Zhang C, Zhou L (2013) Modeling of tool wear for ball end milling cutter based on shape mapping. Int J Interact Des Manuf 7(3):171–181CrossRefGoogle Scholar
  7. 7.
    Khan MR (2011) Geometric modeling, design and analysis of custom-engineered milling cutters. JabalpurGoogle Scholar
  8. 8.
    Huang Y, Liang SY (2004) Modeling of CBN tool flank wear progression in finish hard turning. J Manuf Sci Eng 126(1):98–106CrossRefGoogle Scholar
  9. 9.
    Li KM, Liang SY (2012) Flank wear model for near dry turning under built-up edge effect. J Chn Mech Eng 33(2):123–132Google Scholar
  10. 10.
    Teitenberg TM, Bayoumi AE, Yucesan G (1992) Tool wear modeling through an analytic mechanistic model of milling processes. Wear 154(2):287–304CrossRefGoogle Scholar
  11. 11.
    Okamoto Y, Yazawa T, Kato T, Nishida K, Moriyama S, Maeda Y et al (2017) Study on tool wear in-process estimation for ball end mill using rotation control air turbine spindle. Key Eng Mater 749:94–100CrossRefGoogle Scholar
  12. 12.
    Mou T (2009) Research on tool wear of high speed milling Ti6Al4V. J Shandong Univ, JinanGoogle Scholar
  13. 13.
    Jiang B, Zheng M, Yang S (2003) Establishment of mathematical model for service life of ball-end milling cutter. Manuf Technol Mach Tools 8:9–11Google Scholar
  14. 14.
    Li Y, Deng J, Shi L (2007) Tool materials for high speed machining of titanium alloys. Manuf Technol Mach Tools 8:24–27Google Scholar
  15. 15.
    Sun Y, Sun J, Li J (2016) Finite element prediction analysis of tool wear in titanium milling. Aust J Mech Eng 52:193–201CrossRefGoogle Scholar
  16. 16.
    Rabinowicz E, Dunn LA, Russell PG (1961) A study of abrasive wear under three-body conditions. Wear 4(5):345–355CrossRefGoogle Scholar
  17. 17.
    Lee M (1983) High temperature hardness of tungsten carbide. Metall Trans A 14:1625–1629CrossRefGoogle Scholar
  18. 18.
    Guo B, Zhang L, Cao L, Zhang T, Jiang F, Yan L (2018) The correction of temperature-dependent Vickers hardness of cemented carbide base on the developed high-temperature hardness tester. J Mater Process Technol 255:426–433CrossRefGoogle Scholar
  19. 19.
    Milman YV, Luyckx S, Northrop IT (1999) Influence of temperature, grain size and cobalt content on the hardness of WC–Co alloys. Int J Refract Met Hard Mater 17:39–44CrossRefGoogle Scholar
  20. 20.
    Genghuang H (2013) Research on high-efficiency cutting and tool technology for large barrel sections. Harbin Univ Sci Technol, HarbinGoogle Scholar
  21. 21.
    Milman YV, Chugunova S, Goncharuck V, Luyckx S, Northrop IT (1997) Low and high temperature hardness of WC-6 wt%Co alloys. Int J Refract Met Hard Mater 15:97–101CrossRefGoogle Scholar
  22. 22.
    Kramer BM, Judd PK (1985) Computational design of wear coatings. J Vac Sci Technol A Vac Surf Films 3:2439–2444CrossRefGoogle Scholar
  23. 23.
    Tang L, Xie L, Ma S et al (2010) Experimental research on machining TC4 titanium alloy engine blade ball knife. Manuf Technol Mach Tools 2:92–94Google Scholar
  24. 24.
    Fick A (1995) On liquid diffusion. J Membr Sci 100(1):33–38CrossRefGoogle Scholar
  25. 25.
    Miller FP, Vandome AF, McBrewster J (2010) Fick’s laws of diffusion. Alphascript Publishing, GermanyGoogle Scholar
  26. 26.
    Ezugwu EO, Wang ZM (1997) Titanium alloys and their machinability—a review. J Mater Process Technol 68:262–274CrossRefGoogle Scholar
  27. 27.
    Sui SC, Feng PF, Mou WP (2016) Temperature modeling analysis for milling of titanium alloy. Key Eng Mater 693:928–935CrossRefGoogle Scholar
  28. 28.
    Li L, Chang H, Wang M, Zuo DW, He L (2004) Temperature measurement in high speed milling Ti6Al4V. Key Eng Mater 259:804–808CrossRefGoogle Scholar
  29. 29.
    Ji S, Ni J, Zhan B et al (2016) Orthogonal experimental study on milling temperature of TC4 titanium alloy. Manuf Technol Mach Tools 2:91–93Google Scholar
  30. 30.
    Yang Y, Zhao W, Li L et al (2014) Experimental study on cutting force and cutting temperature of Ti6Al4V titanium alloy in large feed milling. Aviat Precis Manufac Technol 50:34–37Google Scholar
  31. 31.
    Cui D, Zhang D, Wu B, Luo M (2017) An investigation of tool temperature in end milling considering the flank wear effect. Int J Mech Sci 131:613–624CrossRefGoogle Scholar
  32. 32.
    Han M, Li Y, Zhao W (2008) Experimental study on cutting temperature of Ti6Al4V titanium alloy during high speed cutting. Tool Technol 42:10–13Google Scholar
  33. 33.
    Attanasio A, Ceretti E, Rizzuti S, Umbrello D, Micari F (2008) 3D finite element analysis of tool wear in machining. CIRP Ann Manuf Technol 57:61–64CrossRefGoogle Scholar
  34. 34.
    Anto T, Anil PM (2013) An analysis on the influence of temperature on the sliding wear of components finished by grinding and milling processes. Proc Int Conf Energ Effic Technol SustainGoogle Scholar
  35. 35.
    Komanduri R (1982) Some clarifications on the mechanics of chip formation when machining titanium alloys. Wear 76:15–34CrossRefGoogle Scholar
  36. 36.
    Jiang H (2005) A cobalt diffusion based model for predicting crater wear of carbide tools in machining titanium alloys. J Eng Mater Technol 127:136–144CrossRefGoogle Scholar
  37. 37.
    Yue C, Gao H, Liu X, Liang SY, Wang L (2019) A review of chatter vibration research in milling. Chin J Aeronaut 32:1–28CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  • Zemin Zhao
    • 1
  • Xianli Liu
    • 1
    Email author
  • Caixu Yue
    • 1
  • Hongyan Zhang
    • 1
  • Rongyi Li
    • 1
  • Steven Y. Liang
    • 2
  1. 1.School of Mechanical Power EngineeringHarbin University of Science and TechnologyHarbinChina
  2. 2.School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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