Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered

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Abstract

Studies on specific cutting energy, damage equivalent stress, and surface roughness were conducted to identify the optimum cutting parameter area in intermittent hard turning. The optimum cutting parameter area was acquired based on finite element simulations, micromechanics, damage mechanics, and intermittent turning tests. It was found that the transient specific cutting energy and the transient damage equivalent stress evolved cyclically with the periodical formation of saw-tooth chip. The average specific cutting energy in the cutting period became larger as tool wear increased. However, the average damage equivalent stress in the cutting period and surface roughness decreased first and then increased when tool wear became higher. The evolution process of these average values and surface roughness with tool wear can be divided into three stages. There were obvious corresponding relationships between these three stages and the tool wear stages. Analysis of the mean values of specific cutting energy, damage equivalent stress, and surface roughness in the steady tool wear stage indicated that when the feed rate was in the range of 0.2 to 0.25 mm/r and cutting speeds ranging from 110 to 125 m/min were adopted, relatively low energy consumption, relatively long tool life, and relatively good surface quality can be obtained at the same time.

Keywords

Cutting parameters Intermittent turning Specific cutting energy Damage equivalent stress Surface roughness 

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Notes

Acknowledgements

This project is supported by the National Natural Science Foundation of China (Grant No. 51505132) and China Postdoctoral Science Foundation (Grant Nos. 2016T90666 and 2015M580628).

References

  1. 1.
    Li W, Zein A, Kara S, Herrmann C (2011) An investigation into fixed energy consumption of machine tools. In: Glocalized solutions for sustainability in manufacturing. Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Braunschweig, p 268–273Google Scholar
  2. 2.
    Rajemi MF, Mativenga PT, Aramcharoen A (2010) Sustainable machining: selection of optimum turning conditions based on minimum energy considerations. J Clean Prod 18(10):1059–1065CrossRefGoogle Scholar
  3. 3.
    Bayoumi AE, Yücesan G, Hutton DV (1994) On the closed form mechanistic modeling of milling: specific cutting energy, torque, and power. J Mater Eng Perform 3(1):151–158CrossRefGoogle Scholar
  4. 4.
    Lemaitre J, Desmorat R (2005) Engineering damage mechanics: ductile, creep, fatigue and brittle failures. Springer, BerlinGoogle Scholar
  5. 5.
    Camposeco-Negrete C (2015) Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod 91:109–117CrossRefGoogle Scholar
  6. 6.
    Pawade RS, Sonawane HA, Joshi SS (2009) An analytical model to predict specific shear energy in high-speed turning of Inconel 718. Int J Mach Tools Manuf 49(12):979–990CrossRefGoogle Scholar
  7. 7.
    Grzesik W, Denkena B, Żak K, Grove T, Bergmann B (2016) Energy consumption characterization in precision hard machining using CBN cutting tools. Int J Adv Manuf Technol 85(9–12):2839–2845CrossRefGoogle Scholar
  8. 8.
    Cui X, Wang D, Guo J (2016) Performance optimization for cemented carbide tool in high-speed milling of hardened steel with initial microstructure considered. Int J Mech Sci 114:52–59CrossRefGoogle Scholar
  9. 9.
    Nalbant M, Gökkaya H, Sur G (2007) Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Mater Des 28(4):1379–1385CrossRefGoogle Scholar
  10. 10.
    Sayuti M, Sarhan AAD, Salem F (2014) Novel uses of SiO2 nano-lubrication system in hard turning process of hardened steel AISI4140 for less tool wear, surface roughness and oil consumption. J Clean Prod 67:265–276CrossRefGoogle Scholar
  11. 11.
    Wang X, Feng CX (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20(5):348–356CrossRefGoogle Scholar
  12. 12.
    Grzesik W (2008) Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools. Wear 265(3–4):327–335CrossRefGoogle Scholar
  13. 13.
    Sun S, Brandt M, Mo JPT (2014) Evolution of tool wear and its effect on cutting forces during dry machining of Ti-6Al-4V alloy. Proc Inst Mech Eng B J Eng Manuf 228(2):191–202CrossRefGoogle Scholar
  14. 14.
    Wagner V, Baili M, Dessein G (2015) The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting. Int J Adv Manuf Technol 76(5–8):893–912CrossRefGoogle Scholar
  15. 15.
    Wang C, Xie Y, Zheng L, Qin Z, Tang D, Song Y (2014) Research on the chip formation mechanism during the high-speed milling of hardened steel. Int J Mach Tools Manuf 79:31–48CrossRefGoogle Scholar
  16. 16.
    Özel T, Ulutan D (2014) Effects of machining parameters and tool geometry on serrated chip formation, specific forces and energies in orthogonal cutting of nickel-based super alloy Inconel 100. Proc Inst Mech Eng B J Eng Manuf 228(7):673–686CrossRefGoogle Scholar
  17. 17.
    Cantero JL, Díaz-Álvarez J, Miguélez MH, Marín NC (2013) Analysis of tool wear patterns in finishing turning of Inconel 718. Wear 297(1):885–894CrossRefGoogle Scholar
  18. 18.
    Debnath S, Reddy MM, Yi QS (2016) Influence of cutting fluid conditions and cutting parameters on surface roughness and tool wear in turning process using Taguchi method. Measurement 78:111–119CrossRefGoogle Scholar
  19. 19.
    Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1–4):371–393CrossRefGoogle Scholar
  20. 20.
    Grzesik W (2009) Wear development on wiper Al2O3–TiC mixed ceramic tools in hard machining of high strength steel. Wear 266(9):1021–1028CrossRefGoogle Scholar
  21. 21.
    Cui X, Guo J, Zheng J (2016) Optimization of geometry parameters for ceramic cutting tools in intermittent turning of hardened steel. Mater Des 92:424–437CrossRefGoogle Scholar
  22. 22.
    Cui X, Guo J (2017) Effects of cutting parameters on tool temperatures in intermittent turning with the formation of serrated chip considered. Appl Therm Eng 110:1220–1229CrossRefGoogle Scholar
  23. 23.
    Özel T, Zeren E (2005) Finite element method simulation of machining of AISI 1045 steel with a round edge cutting tool. Proceedings of the 8th CIRP International Workshop on Modeling of Machining Operations, Chemnitz, Germany, pp 533–542Google Scholar
  24. 24.
    Shaw MC (1997) Metal cutting principles. Clarendon Press, OxfordGoogle Scholar
  25. 25.
    Zhao J, Yuan X, Zhou Y (2010) Cutting performance and failure mechanisms of an Al2O3/WC/TiC micro-nano-composite ceramic tool. Int J Refract Met Hard Mater 28(3):330–337CrossRefGoogle Scholar
  26. 26.
    Cui X, Zhao J, Zhou Y, Zheng G (2013) Damage mechanics analysis of failure mechanisms for ceramic cutting tools in intermittent turning. Eur J Mech A Solids 37:139–149CrossRefGoogle Scholar
  27. 27.
    Ashby MF, Hallam SD (1986) The failure of brittle solids containing small cracks under compressive stress states. Acta Mater 34(3):497–510CrossRefGoogle Scholar
  28. 28.
    Horii H, Nemat-Nasser S (1986) Brittle failure in compression: splitting, faulting, and brittle-ductile transition. Philos Trans R Soc A Math Phys Eng Sci 319:337–374CrossRefMATHGoogle Scholar
  29. 29.
    Kemeny JM (1991) A model for non-linear rock deformation under compression due to sub-critical growth. Int J Rock Mech Min Sci Geomech Abstr 28(6):459–467CrossRefGoogle Scholar
  30. 30.
    Li HB, Zhao J, Li TJ (2000) Micromechanical modelling of the mechanical properties of a granite under dynamic uniaxial compressive loads. Int J Rock Mech Min Sci 37(6):923–935CrossRefGoogle Scholar
  31. 31.
    Lemaitre J, Lippmann H (1992) A course on damage mechanics. Springer, BerlinCrossRefGoogle Scholar
  32. 32.
    Ravichandran G, Chen W (1991) Dynamic behavior of brittle materials under uniaxial compression. In: Kim KS (ed) Experiments in micromechanics of fracture resistant materials, AMD-130. American Society of Mechanical Engineers, New York, pp 85–90Google Scholar
  33. 33.
    Ravichandran G, Subhash G (1995) A micromechanical model for high strain rate behavior of ceramics. Int J Solids Struct 32:2627–2646CrossRefMATHGoogle Scholar
  34. 34.
    Nemat-Nasser S, Obata M (1988) A microcrack model of dilatancy in brittle materials. J Appl Mech 55(1):24–35CrossRefGoogle Scholar
  35. 35.
    Fredrich JT, Evans B, Wong TF (1990) Effect of grain size on brittle and semibrittle strength: implications for micromechanical modelling of failure in compression. J Geophys Res 95:10907–10920CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical and Power EngineeringHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.School of Energy Science and EngineeringHenan Polytechnic UniversityJiaozuoPeople’s Republic of China

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