Chapter 5 Conclusions and future works
5.2 Future works
In stability lobe diagram, this research could only compute stability under straight cutting. The effect on turn can be added to consideration. However, it will increase the complexity and computing time, which has already been time-consuming in this research.
Therefore, how to improve computing efficiency is also a problem to be solved.
In impact test, the frequency response of hammer decreases in high frequency. It causes the error of stability lobe diagram if the mode of spindle is upper than 1 kHz. In addition, the spindle modes may change with spindle speed. Operation modal analysis, which identifies modal properties during operating condition, may have an opportunity to perform better.
In model training, data collection is also serious time-consuming. Although stable data can collect by cutting under the bound of stability lobe diagram, transition data and slight chatter data are hard to find if stability lobe diagram has errors. The research attempt can develop an automatic data collecting method in future.
REFERENCE
[1] H. Cao, X. Zhang, and X. Chen, “The concept and progress of intelligent spindles:
A review,” International Journal of Machine Tools and Manufacture, vol. 112, pp.
21–52, 2017.
[2] F. W. Taylor, On the art of cutting metals. New York: American Society of Mechanical Engineers, 1907, p. 148.
[3] Y. Fu, Y. Zhang, H. Zhou, D. Li, H. Liu, H. Qiao, and X. Wang, “Timely online chatter detection in end milling process,” Mechanical Systems and Signal Processing, vol. 75, pp. 668–688, 2016.
[4] J. Tlusty and F. Ismail, “Basic Non-Linearity in Machining Chatter,” CIRP Annals, regenerative chatter by modelling and analysis of high-speed milling, ” International Journal of Machine Tools and Manufacture, vol. 43, no. 14, pp. 1437–
1446, 2003.
[8] E. Solis, C. Peres, J. Jiménez, J. Alique, and J. Monje, “A new analytica l–
experimental method for the identification of stability lobes in high-speed milling,” International Journal of Machine Tools and Manufacture, vol. 44, no. 15, pp. 1591–1597, 2004.
[9] S. D. Merdol and Y. Altintas, “Multi Frequency Solution of Chatter Stability for Low Immersion Milling,” Journal of Manufacturing Science and Engineering, vol.
126, no. 3, pp. 459–466, 2004.
[10] T. Insperger and G. Stépán, “Semi-discretization method for delayed systems,” International Journal for Numerical Methods in Engineering, vol. 55, no.
5, pp. 503–518, 2002.
[11] J. Muñoa, M. Zatarain, Z. Dombovari and Y. Yang, “Effect of Mode Interaction on Stability of Milling Processes,” 12th CIRP Conference on Modelling of Machining Operations, San Sebastian, Spain, 2009.
[12] R. Du, M. Elbestawi, and B. Ullagaddi, “Chatter detection in milling based on the probability distribution of cutting force signal,” Mechanical Systems and Signal Processing, vol. 6, no. 4, pp. 345–362, 1992.
[13] E. Soliman and F. Ismail, “Chatter detection by monitoring spindle drive current,” The International Journal of Advanced Manufacturing Technology, vol.
13, no. 1, pp. 27–34, 1997.
[14] C. Toh, “Vibration analysis in high speed rough and finish milling hardened steel,” Journal of Sound and Vibration, vol. 278, no. 1-2, pp. 101–115, 2004.
[15] S. Tangjitsitcharoen, “In-process monitoring and detection of chip formation and chatter for CNC turning,” Journal of Materials Processing Technology, vol. 209, no. 10, pp. 4682–4688, 2009.
[16] Z. Han, H. Jin, M. Li and H. Fu, “An open modular architecture controller based online chatter suppression system for CNC milling,” Mathematical Problems in Engineering, vol. 2015, 2015.
[17] M. C. Yoon and D. H. Chin, “Cutting force monitoring in the endmilling operation for chatter detection,” Proceedings of the Institution of Mechanical Engineers, Part
B: Journal of Engineering Manufacture, vol. 219, no. 6, pp. 455–465, 2005.
[18] Z. Yao, D. Mei, and Z. Chen, “On-line chatter detection and identification based on wavelet and support vector machine,” Journal of Materials Processing Technology, vol. 210, no. 5, pp. 713–719, 2010.
[19] Y. Sun, C. Zhuang, and Z. Xiong, “Real-time chatter detection using the weighted wavelet packet entropy,” 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1652–1657, 2014.
[20] Y. Sun and Z. Xiong, “An Optimal Weighted Wavelet Packet Entropy Method With Application to Real-Time Chatter Detection,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 4, pp. 2004–2014, 2016.
[21] H. Cao, Y. Lei, and Z. He, “Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform,” International Journal of Machine Tools and Manufacture, vol. 69, pp. 11–19, 2013.
[22] G. G. Yen and K. C. Lin, "Wavelet packet feature extraction for vibration monitoring," IEEE Transactions on Industrial Electronics, vol. 47, no. 3, pp. 650-667, 2000.
[23] S. Tangjitsitcharoen, T. Saksri, and S. Ratanakuakangwan, “Advance in chatter detection in ball end milling process by utilizing wavelet transform,” Journal of Intelligent Manufacturing, vol. 26, no. 3, pp. 485–499, Apr. 2013.
[24] X. Q. Li, Y. S. Wong, and A. Y. C. Nee, “A Comprehensive Identification of Tool Failure and Chatter Using a Parallel Multi-ART2 Neural Network,” Journal of Manufacturing Science and Engineering, vol. 120, no. 2, p. 433, 1998.
[25] M. Lamraoui, M. Barakat, M. Thomas, and M. E. Badaoui, “Chatter detection in milling machines by neural network classification and feature selection,” Journal
[26] J. Hino, S. Okubo, and T. Yoshimura, “Chatter Prediction in End Milling by FNN Model with Pruning,” JSME International Journal Series C-Mechanical Systems Machine Elements and Manufacturing, vol. 49, no. 3, pp. 742–749, 2006.
[27] R. P. H. Faassen, “Chatter prediction and control for high speed milling: modeling and experiments,” Ph.D. Thesis, Eindhoven University of Technology, 2007.
[28] T. Insperger and G. Stépán, “Stability of the milling process,” Periodica Polytechnica Mechanical Engineering, vol. 44, no. 1, pp. 47–57, 2000.
[29] G. Stépán, “Modelling nonlinear regenerative effects in metal cutting,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 359, no. 1781, pp. 739–757, 2001.
[30] X.-H. Long, B. Balachandran, and B. P. Mann, “Dynamics of milling processes with variable time delays,” Nonlinear Dynamics, vol. 47, no. 1-3, pp. 49–63, 2006.
[31] T. Insperger, Semi-discretization for time-delay systems: stability and engineering applications. Place of publication not identified: Springer-Verlag New York, 2014.
[32] T. Insperger and G. Stépán, "Updated semi-discretization method for periodic delay-differential equations with discrete delay", International Journal for Numerical Methods in Engineering, vol. 61, no. 1, pp. 117-141, 2004.
[33] O. Bobrenkov, E. Butcher and B. Mann, "Application of the Liapunov–Floq uet transformation to differential equations with time delay and periodic coefficients", Journal of Vibration and Control, vol. 19, no. 4, pp. 521-537, 2012.
[34] T. Insperger, G. Stépán, P. Bayly, and B. Mann, “Multiple chatter frequencies in milling processes,” Journal of Sound and Vibration, vol. 262, no. 2, pp. 333–345, 2003.
[35] P. Goupillaud, A. Grossmann, and J. Morlet, “Cycle-octave and related transforms in seismic signal analysis,” Geoexploration, vol. 23, no. 1, pp. 85–102, 1984.
[36] F. Wasilewski, “Wavelet Browser by PyWavelets,” Daubechies 10 wavelet (db10) properties, filters and functions - Wavelet Properties Browser. [Online]. Availab le :
http://wavelets.pybytes.com/wavelet/db10/.
[37] S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, no. 7, pp. 674–693, 1989.
[38] F. Safara, S. Doraisamy, A. Azman, A. Jantan, and S. Ranga, “Wavelet Packet Entropy for Heart Murmurs Classification,” Advances in Bioinformatics, vol. 2012, pp. 1–6, 2012.
[39] C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal, vol. 27, no. 4, pp. 623–656, 1948.
[40] Z. Li, W. Li and R. Liu, “Applications of Entropy Principles in Power Systems: A Survey,” 2005 IEEE/PES Transmission & Distribution Conference & Exposition:
Asia and Pacific, Dalian, pp. 1-4, 2005.
[41] EFavDB, “EFavDB/svm-classification,” GitHub. [Online]. Availab le : https://github.com/EFavDB/svm-classification.
[42] B. Schölkopf, R.C. Williamson, A. J. Smola, J. Shawe-Taylor and J. Platt, “Support vector method for novelty detection,” Advances in Neural Information Processing Systems 12, pp. 526-532, 1999.
[43] K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Schölkopf, “An introduction to kernel-based learning algorithms,” IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 181–201, 2001.
[44] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF: Identifying Density-Based Local Outliers,” Proceedings of the 2000 ACM SIGMOD international
[45] D. L. Olson and D. Delen, Advanced data mining techniques. Berlin: Springer, pp.137–138, 2008.
[46] E. Budak and Y. Altintaş, “Analytical Prediction of Chatter Stability in Milling—
Part I: General Formulation,” Journal of Dynamic Systems, Measurement, and Control, vol. 120, no. 1, pp. 22–30, 1998.
[47] B. Patel, B. Mann, and K. Young, “Uncharted islands of chatter instability in milling,” International Journal of Machine Tools and Manufacture, vol. 48, no. 1, pp. 124–134, 2008.
[48] T. Choi and Y. C. Shin, “On-Line Chatter Detection Using Wavelet-Based Parameter Estimation,” Journal of Manufacturing Science and Engineering, vol.
125, no. 1, pp. 21–28, 2003.