Experimental Evaluation of Influence of Tool Wear on Quality of Turning

  • V. P. LapshinEmail author
  • T. S. Babenko
  • D. V. Moiseev
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


This article is devoted to the evaluation of the effect of tool wear on the quality of the machined surface in turning on the metal cutting machines. The modern level of measuring equipment allows to obtain a qualitative assessment of the roughness indices of the treated surface. For the experiment, the Triebworx T4HD profilometer-contourograph and the optical three-dimensional microscope Contour ELITE were used as measuring instruments. The equipment was provided by Optec. The results of the experiment made it possible to obtain a qualitative tool wear characteristic on the back face and to associate it with a characteristic that reflects the changes in the surface quality of a part in time. As the main conclusion formed by the results of processing the data obtained in the experiment, it is argued that the quality of the treated surface depends little on the degree of tool wear until the moment of approaching the catastrophic level of wear. With the approach of wear to the catastrophic point and its further increase, the quality of the treated surface sharply deteriorates. The dependencies obtained in the work as a whole coincide with the realistic approach to the evaluation of the dynamics of the processes taking place in metal cutting machines. According to this approach, during the cutting process, several evolutionary sections are observed: the tool run-in and stabilization area, both of the dynamics of the control system and the quality of the parts produced, and the sharp increase in the tool wear (catastrophic wear) in which the dynamics of the system becomes unstable, and the quality of the treated surface sharply deteriorates.


Surface quality Turning Wear Roughness Metal cutting machine 


  1. 1.
    Kudinov VA (1967) Dynamics of machine tools. Mashinostroenie, Moscow (in Russ.)Google Scholar
  2. 2.
    Zharkov IG (1986) Vibration when machining the edge tool. Mashinostroenie, Moscow (in Russ.)Google Scholar
  3. 3.
    Kedrov SS (1978) Vibrations of machine tools. Mashinostroenie, Moscow (in Russ.)Google Scholar
  4. 4.
    Ryzhkin AA, Shuchev KG, Klimov (2008) Processing of materials by cutting, Feniks, Rostov-on-Don (in Russ.)Google Scholar
  5. 5.
    GOST 2789-73 (1985) The roughness of the surface. Parameters, characteristics and notation. Izdatelstvo standartov, Moscow (in Russ.)Google Scholar
  6. 6.
    Nikhil R. Dhar, Md. Kamruzzaman, Soumitra Paul (2006) Wear behavior of uncoated carbide inserts under dry, wet and cryogenic cooling conditions in turning C-60 steel. J Braz Soc Mech Sci Eng XXVIII(2):146–152Google Scholar
  7. 7.
    Shulepov AV, Kholin IE et al (2014) Developmentof a measurement complex for automatic adjustment of tool settings for programmed numerically controlled machine tools. Meas Tech 56(12):1377–1381CrossRefGoogle Scholar
  8. 8.
    Teleshevskii VI, Sokolov VA (2012) Laser correction of geometric errors of multi-axis programmed-controlled systems. Meas Tech 55(5):535–541CrossRefGoogle Scholar
  9. 9.
    Grigoriev SN, Teleshevsky VI, Sokolov VA (2013) Volumetric geometric accuracy improvement for multi-axis systems based on laser software error correction. In: International conference on competitive manufacturing (COMA ‘13), pp 101–106Google Scholar
  10. 10.
    Maksin YA, Teleshevskii VI, Temnikov PV (2011) System for computer aided design and fabrication of means of linear angular measurement based on three dimensional parametric modeling. Meas Tech 54(8):869–873CrossRefGoogle Scholar
  11. 11.
    Faga MG, Mattioda R, Settineri L (2010) Microstructural and mechanical characteristics of recycled hard metals for cutting tools. CIRP Ann-Manuf Technol 59(1):133–136CrossRefGoogle Scholar
  12. 12.
    Hu J, Chou YK (2007) Characterizations of cutting tool flank wear-land contact. Wear T 263(7):1454–1458CrossRefGoogle Scholar
  13. 13.
    Igolkin AA, Musaakhunova LF, Shabanov KY (2015) Method development of the vibroacoustic characteristics calculation of the gas distribution stations elements. Procedia Eng 106:309–315CrossRefGoogle Scholar
  14. 14.
    Karayel D (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209(7):3125–3137CrossRefGoogle Scholar
  15. 15.
    Liu J et al (2007) Study on lubricating characteristic and tool wear with water vapor as coolant and lubricant in green cutting. Wear 262(3):442–452CrossRefGoogle Scholar
  16. 16.
    Liu Y et al (2010) Application of hierarchical model method on open CNC system’s behavior reconstruction. ICINCO 3:172–175Google Scholar
  17. 17.
    Stepan G (1998) Delay-differential equation models for machine tool chatter. In: Moon FC (ed) Nonlinear dynamics of material processing and manufacturing. Wiley, NY, pp 165–192Google Scholar
  18. 18.
    Stepan G, Insperg T, Szalai Delay R (2005) Parametric excitation, and the nonlinear dynamics of cutting processes. Int J Bifurcat Chaos 15(9):2783–2798MathSciNetCrossRefGoogle Scholar
  19. 19.
    Lapshin VP, Turkin IA (2013) Effect of spindle servo drive properties on drilling dynamics of deep pinholes. Vestn DSTU 5–6:56–65 (in Russ.)Google Scholar
  20. 20.
    Lapshin VP, Turkin IA (2015) Dynamic influence of the spindle servo drive on the drilling of deep narrow holes. Russ Eng Res 35(10):795–797. Scholar
  21. 21.
    Zakovorotny VL, Lapshin VP, Babenko TS (2017) Assessing the regenerative effect impact on the dynamics of deformation movements of the tool during turning. Procedia Eng 206:68–73. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • V. P. Lapshin
    • 1
    Email author
  • T. S. Babenko
    • 1
  • D. V. Moiseev
    • 1
  1. 1.Don State Technical UniversityRostov-on-DonRussia

Personalised recommendations