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Hilbert–Huang Transform-Based Emitted Sound Signal Analysis for Tool Flank Wear Monitoring

  • Research Article - Mechanical Engineering
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Abstract

This paper presents an emitted sound signal analysis technique for tool flank wear monitoring based on the Hilbert–Huang Transform (HHT). HHT is a new signal processing technique suitable for analyzing non-stationary and non-linear signals like emitted sound. The need for HHT in this analysis and its principle are explained. The entire experiment was done on a conventional turning machine using carbide insert tools and mild steel work piece. The emitted sound signal during turning process of a fresh tool, a slightly worn tool with 0.2 mm flank wear and a severely worn tool with 0.4 mm flank wear were recorded separately using a highly sensitive microphone under different cutting conditions. Each emitted sound signal is decomposed into several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). The Hilbert transform is then applied on each IMF to obtain the instantaneous frequencies with time and their amplitudes. Finally, the marginal and the Hilbert spectrums of fresh, slightly worn and severely worn tool sound signals were produced using selected IMFs. From these spectrums, it is found that the increase in tool flank wear resulted in an increase of the sound pressure amplitude. This is also found true for all the different cutting conditions. The results show that the HHT-based emitted sound signal analysis can also be considered as a simple and reliable method for tool flank wear monitoring.

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References

  1. Silva R.G., Reuben R.L., Baker K.J., Wilcox S.J.: Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors. Mech. Syst. Signal. Pr. 12, 319–332 (1998)

    Article  Google Scholar 

  2. Salgado D.R., Alonso F.J.: An approach based on current and sound signals for in-process tool wear monitoring. Int. J.Mach. Tool. Manu. 47(14), 2140–2152 (2007)

    Article  Google Scholar 

  3. Lu,M.-C.; Elijah K.-A.: Analysis of Sound Signal Generation Due to Flank Wear in Turning. J. Manu. Sci. Eng. 124(4), 799 (10 pages) doi:10.1115/1.1511177 (2002)

  4. Alonso F.J., Salgado D.R.: Application of singular spectrum analysis to tool wear detection using sound signals, Proceedings IME. Part B J. Eng. Manu. 219(9), 703–710 (2005)

    Article  Google Scholar 

  5. Peng, Z.K.; Peter W.T.; Chu, F.L.: An improved Hilbert-Hung transform and its application in vibration signal analysis. J. Sound. Vibration. 286(1–2), 187–205

  6. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.L.C.; Shih, H.H.; Zheng, Q.N;, Yen, N.C.; Tung, C.C.; Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.In: Proceeding of the Royal Society of London Series A—Mathematical Physical and Engineering Sciences, pp. 903–995 (1998)

  7. Peng, Z.K; Peter, W.T.; Chu, F.L.: A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech. Syst. Signal Pr. 19(5) 974–988

  8. Lisha, S.; Minfen, S.; Francis H. Y. Chan: A method for estimating the instantaneous frequency of non-stationary heart sound signals.In: Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol. 1, pp. 798–801 (2003)

  9. Yuping, Z.: Hilbert-Huang: Transform and Marginal Spectrum for Detection of Bearing Localized Defects.In: Proceedings of the 6th World Congress on Intelligent Control and Automation, pp. 5457–5461. Dalian (2006)

  10. Titchmarsh, E.C.: Introduction to the theory of Fourier integrals. Oxford University Press (1948)

  11. Rilling, G.; Flandrin, P.; Gonçalv‘es, P.: On Empirical Mode Decomposition and its Algorithms.In: Procedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP2003). Grado, Italy (2003)

  12. Kopac J., Sali S.: Tool wear monitoring during the turning process, J. Mater. Pr. Technol. 113(1–3), 312–316 (2001)

    Article  Google Scholar 

  13. Altina, M.; Nalbantb, A.; Taskesenb: The effects of cutting speed on tool wear and tool life when machining Inconel 718 with ceramic tools, ScienceDirect: Mater. Design. 28(9), 2518–2522 (2007)

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Correspondence to J Emerson Raja.

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Raja, J.E., Kiong, L.C. & Soong, L.W. Hilbert–Huang Transform-Based Emitted Sound Signal Analysis for Tool Flank Wear Monitoring. Arab J Sci Eng 38, 2219–2226 (2013). https://doi.org/10.1007/s13369-013-0580-7

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  • DOI: https://doi.org/10.1007/s13369-013-0580-7

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