Chatter identification in turning process based on vibration analysis using Hilbert-Huang transform
DOI:
https://doi.org/10.15282/jmes.14.2.2020.25.0537Keywords:
chatter, turning, time-frequency analysis, Hilbert-Huang transform, short-time Fourier transformAbstract
In machining process, the increasing cutting depth is aimed to accelerate machining operations in a short time. However, the increasing cutting depth can cause chatter vibration at any time. Chatter can accelerate tool wear and lead to poor machined surface. This paper studied the application of Hilbert-Huang transform (HHT) to analyse chatter caused by the increasing cutting depth during operation. Chatter vibration is typical of non-stationary and non-linear vibration signals, therefore it should be analysed by appropriate signal processing; HHT. In this research, initially “hammering tests” were conducted to observe dynamic modal parameters of cutting system; modal mass, damping ratio, and stiffness. Then stability lobe chart was generated based on those dynamic modal parameters to determine cutting parameters. Second, turning tests were conducted and then vibrations obtained in turning tests were analysed using HHT for chatter detection. The results were then compared by conventional signal processing; short-time Fourier (STFT) transforms. The results show that the empirical mode decomposition (EMD) of HHT process separated complex vibrations into simple components and isolated chatter from the others. The chatter was isolated in the first IMF. Therefore, chatter can be identified by EMD process. In the spectrum analysis, HHT spectra showed its superiority over STFT spectra. HHT spectra provided high time resolution and high frequency resolution both rather than STFT spectra that provided blurry and blocked spectra. The implication is that HHT can be applied to monitor the machining process.
References
N. Ambhore, D. Kamble, and S. Chinchanikar, "Analysis of tool vibration and surface roughness with tool wear progression in hard turning: An experimental and statistical approach," Journal of Mechanical Engineering and Sciences, vol. 14. pp. 6461-6472, 2020, doi: https://doi.org/10.15282/jmes.14.1.2020.21.0506.
A. R. Yusoff, M.R.Z.M. Suffian, and M. Y. Thaib, "Literature review of optimization technique for chatter suppression in machining," Journal of Mechanical Engineering and Sciences, vol. 1. pp. 47-61, 2011.
N. Jamil, A. R. Yusoff, and M. H. Mansor, "Literature review of electromagnetic actuator force generation for dynamic modal testing applications," Journal of Mechanical Engineering and Sciences, vol. 3. pp. 311-319, 2012, doi: http://dx.doi.org/10.15282/jmes.3.2012.7.0029.
X. Dang, M. Wan, Y. Yang, and W. Zhang, “Efficient prediction of varying dynamic characteristics in thin-wall milling using freedom and mode reduction methods,” Int. J. Mech. Sci., vol. 150, no. September 2018, pp. 202–216, 2019, doi: 10.1016/j.ijmecsci.2018.10.009.
N. A. Kasim, M. Z. Nuawi, J. A. Ghani, M. Rizal, M. A. F. Ahmad, C. H. Che Haron, "Cutting tool wear progression index via signal element variance," Journal of Mechanical Engineering and Sciences, vol. 13. pp. 4596-4612, 2019. doi: https://doi.org/10.15282/jmes.13.1.2019.17.0387.
H. Ma, J. Wu, L. Yang, and Z. Xiong, “Active chatter suppression with displacement-only measurement in turning process,” J. Sound Vib., vol. 401, pp. 255–267, 2017, doi: 10.1016/j.jsv.2017.05.009.
H. Li and Y. Chen, Handbook of Manufacturing Engineering and Technology. Springer-Verlag London, 2014.
K. Kolluru and D. Axinte, “Coupled interaction of dynamic responses of tool and workpiece in thin wall milling,” J. Mater. Process. Technol., vol. 213, pp. 1565–1574, 2013, doi: 10.1016/j.jmatprotec.2013.03.018.
Y. Yamada, T. Kadota, S. Sakata, J. Tachibana, and Y. Kakinuma, “Integrated chatter monitoring based on sensorless cutting force/torque estimation in parallel turning,” Int. J. Autom. Technol., vol. 11, no. 2, pp. 215–225, 2017, doi: 10.20965/ijat.2017.p0215.
I. N. Tansel, X. Wang, P. Chen, A. Yenilmez, and B. Ozcelik, “Transformations in machining. Part 2. Evaluation of machining quality and detection of chatter in turning by using s-transformation,” Int. J. Mach. Tools Manuf., vol. 46, pp. 43–50, 2006, doi: 10.1016/j.ijmachtools.2005.04.011.
J. Xu, K. Yamada, K. Seikiya, R. Tanaka, and Y. Yamane, “Effect of different features to drill-wear prediction with back propagation neural network,” Precis. Eng., vol. 38, pp. 791–798, 2014, doi: 10.1016/j.precisioneng.2014.04.007.
S. Gu, J. Ni, and J. Yuan, “Non-stationary signal analysis and transient machining process condition monitoring,” Int. J. Mach. Tools Manuf., vol. 42, pp. 41–51, 2002, doi: 10.1016/S0890-6955(01)00097-9.
H. Cao, K. Zhou, and X. Chen, “Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators,” Int. J. Mach. Tools Manuf., vol. 92, pp. 52–59, 2015, doi: 10.1016/j.ijmachtools.2015.03.002.
T. Kalvoda and Y. R. Hwang, “Analysis of signals for monitoring of nonlinear and non-stationary machining processes,” Sensors Actuators, A Phys., vol. 161, no. 1–2, pp. 39–45, 2010, doi: 10.1016/j.sna.2010.05.032.
R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Ann. - Manuf. Technol., vol. 59, no. 2, pp. 717–739, 2010, doi: 10.1016/j.cirp.2010.05.010.
A. Susanto, C.-H. Liu, K. Yamada, Y.-R. Hwang, R. Tanaka, and K. Sekiya, “Milling process monitoring based on vibration analysis using Hilbert-Huang transform,” Int. J. Autom. Technol., vol. 12, pp. 688–698, 2018, doi: 10.20965/ijat.2018.p0688.
A. Susanto, C. H. Liu, K. Yamada, Y. R. Hwang, R. Tanaka, and K. Sekiya, “Application of Hilbert–Huang transform for vibration signal analysis in end-milling,” Precis. Eng., vol. 53, pp. 263–277, 2018, doi: 10.1016/j.precisioneng.2018.04.008.
A. Susanto, K. Yamada, K. Mani, R. Tanaka, and K. Sekiya, “Vibration analysis in milling of thin-walled workpieces using Hilbert-Huang transform,” in International Conference on Leading Edge Manufacturing in 21st century (LEM21), 2017, doi: 10.1299/jsmelem.2017.9.031.
W. Soong, “Hilbert–Huang Transform-Based Emitted Sound Signal Analysis for Tool Flank Wear Monitoring,” no. March 2017, 2013, doi: 10.1007/s13369-013-0580-7.
P. Wolszczak, G. Litak, and M. Dziuba, “Monitoring of drilling conditions using the Hilbert-Huang transformation,” in Matec web of Conferences, 2018, vol. 148, doi: 10.1051/matecconf/201814816003.
Y. Altintas, Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design. Cambridge University Press, New York, 2012.
N. E. Huang and S. S. P. Shen, Hilbert-Huang Transform and Its Applications. World Scientific, 2005.
G. Zhao, L. Liu, D. Wang, J. Guo, and W. Chen, “Mechanical Properties of AISI 1045 Steel Subjected to Combined Loads of Tension and Torsion,” Exp. Tech., vol. 42, no. 4, pp. 393–406, 2018, doi: https://doi.org/10.1007/s40799-018-0236-3.
T. L. Schmitz and K. S. Smith, Machining dynamics: Frequency response to improved productivity. Springer US, 2009.
M. E. B. M. Lamraoui, M. Thomas, “Cyclostationarity approach for monitoring chatter and tool wear in high speed milling,” Mech. Syst. Signal Process., vol. 44, 2014, doi: 10.1115/1.1308035.
M. Uekita and Y. Takaya, “Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain,” Measurement, vol. 103, pp. 199–207, 2017, doi: 10.1016/j.measurement.2017.02.035.
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