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Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis

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Abstract

It has been established that turning process on a lathe exhibits low dimensional chaos. This study reports the results of nonlinear time series analysis applied to sensor signals captured real time. The purpose of this chaos analysis is to differentiate three levels of flank wears on cutting tool inserts—fresh, partially worn and fully worn—utilizing the single value index extracted from the reconstructed chaotic attractor; the correlation dimension. The analysis reveals distinguishable dynamics of cutting characterized by different values for the dimension of the attractor when different quality tool inserts are used. This dependence can be effectively utilized as one of the indicators in tool condition monitoring in a lathe. This paper presents the experimental results and shows that tool vibration signals can transmit tool wear conditions reliably.

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References

  • Bukkapatnam STS, Lakhtakia A, Kumara SRT (1995a) Analysis of sensor signals shows turning on a lathe exhibits low-dimensional chaos. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Top 52(3):2375–2387. doi:10.1103/PhysRevE.52.2375

    Google Scholar 

  • Bukkapatnam STS, Lakhtakia A, Kumara SRT, Satapathy G (1995b) Characterization of nonlinearity of cutting tool vibrations and chatter. In: ASME symposium on intelligent manufacturing and material processing, vol 69, pp 1207–1223

  • Bukkapatnam STS, Lakhtakia A, Kumara SRT (2000) Fractal estimation of flank wear in turning. J Dyn Syst Meas Control 122:89–94. doi:10.1115/1.482446

    Article  Google Scholar 

  • Dan L, Mathew J (1990) Tool wear and failure monitoring techniques for turning—a review. Int J Mach Tools Manuf 30(4):579–598. doi:10.1016/0890-6955(90)90009-8

    Article  Google Scholar 

  • Dimla DE (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098. doi:10.1016/S0890-6955(99)00122-4

  • Ding M, Grebogi C, Ott E, Sauer T, Yoke JA (1993) Plateau onset for correlation dimension: when does it occur? Phys Rev Lett 70(25):3872–3875. doi:10.1103/PhysRevLett.70.3872

    Article  Google Scholar 

  • Franca LF, Savi MA (2000a) On the time series determination of Lyapunov exponents applied to the nonlinear pendulum analysis. In: Non linear dynamics, chaos. Contr Their Appl Eng Sci ABCM 5:356–366

  • Franca LF, Savi MA (2000b) Estimating fractal dimension from time series: case study of nonlinear pendulum. In: Non linear dynamics, chaos. Contr Their Appl Eng Sci ABCM 5:345–355

  • Fraser AM, Swinney HL (1986) Independent coordinates for strange attractors from mutual information. Phys Rev A 33:1134–1140. doi:10.1103/PhysRevA.33.1134

    Article  MathSciNet  Google Scholar 

  • Govekar E, Grabec I (1998) In: Monitoring and automatic supervision in manufacturing, 5th international conference, Warsaw, p 74

  • Govekar E, Gradisek J, Grabec I (2000) Analysis of acoustic emission signals and monitoring of machining processes. Ultrasonics 38:598–603. doi:10.1016/S0041-624X(99)00126-2

    Article  Google Scholar 

  • Grassberger P, Procaccia I (1983) Characterization of strange attractors. Phys Rev Lett 50:346–349. doi:10.1103/PhysRevLett.50.346

    Article  MathSciNet  Google Scholar 

  • Kang MC, Kim JS, Kim JH (2001) A monitoring technique using a multi-sensor in high speed machining. J Mater Process Technol 113:331–336. doi:10.1016/S0924-0136(01)00698-7

    Article  Google Scholar 

  • Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, UK

    MATH  Google Scholar 

  • Kennel MB, Brown R, Abarbanel HDI (1992) Determining embedding dimension from phase-space reconstruction using a geometrical construction. Phys Rev A 25:3403–3411. doi:10.1103/PhysRevA.45.3403

    Article  Google Scholar 

  • Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60. doi:10.1214/aoms/1177730491

    Article  MATH  MathSciNet  Google Scholar 

  • Moon FC (1987) Chaotic vibrations: an introduction for applied scientists and engineers. Wiley, New York

    MATH  Google Scholar 

  • Moon F (1990) Chaotic and fractal dynamics. Springer, New York

    Google Scholar 

  • Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45(9):712–716. doi:10.1103/PhysRevLett.45.712

    Article  Google Scholar 

  • Reiss JD (2001) The analysis of chaotic time series. Ph.D. thesis, Georgia institute of Technology

  • Sokolowski A, Kosmol J (2001) Selected examples of cutting process monitoring and diagnostics. J Mater Process Technol 113:322–330. doi:10.1016/S0924-0136(01)00623-9

    Article  Google Scholar 

  • Takens F (1980) Detecting strange attractors in turbulence. In: Dynamical systems and turbulence, Warwick. Lecture notes in mathematics, vol 898. Springer, Berlin, pp 366–81

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Acknowledgments

V. G. Rajesh thanks the Institute of Human Resources Development, Trivandrum for supporting his studies. We acknowledge with thanks the All India Council for Technical Education, New Delhi for the financial assistance being received for this work under RPS Project No. PLB1/6723/05.

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  1. Division of Mechanical Engineering, School of Engineering, CUSAT, Cochin University P.O., Cochin, 682022, Kerala, India

    V. G. Rajesh & V. N. Narayanan Namboothiri

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  1. V. G. Rajesh
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  2. V. N. Narayanan Namboothiri
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Correspondence to V. N. Narayanan Namboothiri.

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Rajesh, V.G., Narayanan Namboothiri, V.N. Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis. Soft Comput 14, 913–919 (2010). https://doi.org/10.1007/s00500-009-0466-5

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