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Analysis of Cutting Path Effect on Spindle AE and Vibration Based Tool Wear Monitoring System in Micro Milling
|關鍵字:||微細刀具監控;Micro tool condition monitoring;切削路徑影響;振動訊號;聲射訊號;Cutting path effect;Vibration signal;Acoustic Emission signal||出版社:||機械工程學系所||引用:|| T. Masuzawa, “State of the art of micromachining,” CIRP Annals - Manufacturing Technology, Volume 49, Pages 473–488, 2000  K.F. Ehmann, R.E. DeVor, S.G. Kapoor, “Micro/meso-scale mechanical manufacturing-opportunities and challenges,” Proceedings, JSME/ASME International Conference on Materials and Processing, Volume 1, Pages 6–13, 2002  A.G. Rehorn, J. Jiang, P.E. Orban, ” Sate-of-the-art methods and results in tool condition monitoring: a review,” The International Journal of Advanced Manufacturing Technology, Volume 26, Pages 693–710, 2005  J.V. Abellan-Nebot, F.R. Subirón, “ A review of machining monitoring systems based on artificial intelligence process models,” The International Journal of Advanced Manufacturing Technology, Volume 47, Pages 237–257, 2010  G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. König, R. Teti, “Tool condition monitoring (TCM)-the status of research and industrial application,” CIRP Annals - Manufacturing Technology , Volume 44, Pages 541-567, 1995  K. Matsushima, P. Bertok, T. Sata, “In-process detection of tool breakage by monitoring the spindle motor current of a machine tool,” ASME, Pages 145-153, 1982  Z. Deyuan, D. Shunan, H. Yuntai, C. Dingchang, “Progress of cutting and grinding with some problems in CADCAM, FMS and Mechatronics,” ICPCG 94. In: Proceedings of the First Asia-Pacific and Second Japan-China International Conference on Progress of Cutting and Grinding, Pages 270–276, 1994  Y. Altintas, “Prediction of cutting forces and tool breakage in milling from feed drive current measurements,” ASME Journal of Engineering for Industry, Volume 114, Pages 386–392, 1992  G.D. Kim, C.N. Chu, “Indirect cutting force measurement considering frictional behaviour in a machining centre using feed motor current,” International Journal of Advanced Manufacturing Technology, Volume 15, Pages 478–484, 1999  J.W. Youn, M.Y. Yang, H.Y. Park, “Detection of cutting tool fracture by dual signal measurements,” International Journal of Machine Tools & Manufacture , Volume 34, Pages 507–525, 1994  D.E. Lee, I. Hwang, , C.M.O. Valente, J.F.G. Oliveira, D.A. Dornfeld, “Precision manufacturing process monitoring with acoustic emission,” International Journal of Machine Tools & Manufacture, Volume 46, Pages 176–188, 2006  M.C. Lu, E. Kannatey-Asibu Jr., “Flank wear and process characteristic effect on system dynamics in turning,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, Volume 126, Pages 131-140, 2004  M.C. Lu, E. Kannatey-Asibu Jr., “Analysis of sound signal generation due to flank wear in turning,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, Volume 124, Pages 799-808, 2002  G. Chryssolouris, M. Domroese, P. Beaulieu, “Sensor synthesis for control of manufacturing processes,” Journal of Engineering for Industry, Transactions of the ASME, Volume 114, Pages 158–174, 1992  G.S. Hong, M. Rahman, Q. Zhou, “Using neural network for tool condition monitoring based on wavelet decomposition,” International Journal of Machine Tools and Manufacture, Volume 36, Pages 551–566, 1996  R.X. Du, M.A. Elbestawi, S. Li, “Tool condition monitoring in turning using fuzzy set theory,” International Journal of Machine Tools and Manufacture, Volume 32, Pages 781–796, 1992  E. Kannatey-Asibu, E. Emel, “Linear discriminant function analysis of acoustic emission signals for cutting tool monitoring,” Mechanical Systems and Signal Processing, Volume 1, Pages 333–347, 1987  D.J. Waldorf, S.G. Kapoor, R.E. DeVor, “Automatic recognition of tool wear on a face mill using a mechanistic modeling approach,” Wear, Volume 157, Pages 305-323, 1992  L.P. Heck, J.H. McClellan, “Mechanical system monitoring using hidden Markov models,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Volume 3, Pages 1697–1700, 1991  N. Ikawa, S. Shimada, H. Tanaka, “Minimum thickness of cut in micro-machining,” Nanotechnology, Volume 3, Pages 6–9,1992  M.B. Jun, “Modeling and analysis of micro-end milling dynamics,” Ph.D. Dissertation, University of Illinois Urbana-Champaign, 2005.  K. Zhu, G.S. Hong, Y.S. Wong, W. Wang, “Cutting force denoising in micro-milling tool condition monitoring,” International Journal of Production Research, Volume 46, Pages 4391-4408, 2008  I.N. Tansel, T.T. Arkan, W.Y. Bao, N. Mahendrakar, B. Shisler, D. Smith, M. McCool, “Tool wear estimation in micro-machining. - Part I: tool usage-cutting force relationship,” International Journal of Machine Tools and Manufacture, Volume 40, Pages 599-608, 2000  I.N. Tansel, O. Rodriguez, M. Trujillo, E. Paz, W. Li, “Micro-end-milling—I.Wear and breakage,” International Journal of Machine Tools & Manufacture, Volume 38, Pages 1419–1436, 1998  I.N. Tansel, M. Trujillo, A Nedbouyan, C Velez, Wei-Yu Bao, T.T Arkan, B Tansel, “Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals,” International Journal of Machine Tools & Manufacture, Volume 38, Pages 1449–1466, 1998  W.Y. Bao, I.N. Tansel, “Modeling Micro-end-milling operations. PartIII: Influence of Tool Wear,” International Journal of Machine Tools & Manufacture, Volume 40, Pages 2193–2211, 2000  K. Jemielniak, S. Bombinski, P.X. Aristimuno, “Tool condition monitoring in micro-milling based on hierarchical integration of signal measures,” CIRP Annals - Manufacturing Technology, Volume 57, Pages 121-124, 2008  K. Jemielniak, P.J. Arrazola, “Application of AE and cutting force signals in tool condition monitoring in micro-milling,” CIRP Journal of Manufacturing Science and Technology, Volume 1, Pages 97-102, 2008  M. Malekian, S.S. Park, Martin B.G. Jun, “Tool wear monitoring of micro-milling operations,” Journal of Materials Processing Technology, Volume 209, Pages 4903-4914, 2009  K. Zhu, Y.S. Wong, G.S. Hong, “Multi-category micro-milling tool wear monitoring with continuous hidden Markov models,” Mechanical Systems and Signal Processing, Volume 23, Pages 547-560, 2009  J. Yan, J. Lee, “A hybrid method for on-line performance assessment and life prediction in drilling operations,” Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2007, Pages 2500-2505, 2007  Y. Yao, X. Li, Z. Yuan, “Tool wear detection with fuzzy classification and wavelet fuzzy neural network,” International Journal of Machine Tools & Manufacture, Volume 39, Pages 1525-1538, 1999  C.R. Huang, M.C. Lu, C.E. Lu, Y.W. Hsu, “Study of spindle vibration signals for tool breakage monitoring in micro-drilling,” Proceedings of the World Congress on Intelligent Control and Automation (WCICA) , Pages 1130-1134, 2011  Wavelet Toolbox User''s Guide, 2008, http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavelet/wavelet_ug.pdf  J.W. Cooley, J.W. Tukey, “An Algorithm for the Machine Calculation of Complex Fourier Series,” Math. of Computat., Volume 19, Pages 297- 301  R.J. Schilling, S.L. Harris, Fundamentals of Digital SignalProcessing Using MATLAB, Thomson, 2005  S. Haykin, B.V. Veen, Signals and System, John Wiley & Sons, 2003  C.M. Bishop, Pattern Recognition And Machine Learning,Springer, 2006  S. Theodoridis, K. Koutroumbas, Pattern Recognition, ELSECIER, 2009  顏嘉良，應用類神經網路於微細切削刀具狀態偵測之研究，國立中興大學，碩士論文，2008  謝萬澔，應用類神經網路與振動訊號之微銑刀具狀態偵測系統開發，國立中興大學，碩士論文，2009  黃耀賢，主軸振動與聲射訊號於微銑刀具磨耗監測之應用研究，國立中興大學，碩士論文，2010||摘要:||
As the demand of the small feature and high accuracy for aerospace, biomedical, and electronic devices continuously increases, the micro mechanical machining plays an important role for improving their manufacturing quality and efficiency. Due to the higher tool wear rate than conventional counterpart, the tool wear monitoring in the micro machining draws much more attention than before.
The objective of this thesis is to analyze the cutting path effect on the performance of tool wear monitoring system integrated with the spindle vibration and acoustic emission (AE) signal obtained from the spindle housing, as well as the study of the effect of system parameters on the system performance.
A micro tool condition monitoring system integrated by sensor system, signal transformation, feature selection, and classifier was developed in this study. In which, the FFT transformation was used for transforming the time domain signal to the frequency domains, the class mean scatter criteria was used to select the features closely related to the tool wear condition, and the Fisher linear discriminant function was the basis for designing the classifier. In the analysis of the parameters effect on the system performance, the bandwidth sizes of frequency domain signal, the length sizes of extracted signal, and the change of cutting path in micro milling were studied. In collecting the signal for system analysis and development, an experiment was implemented along with 700 μm diameter micro end mill and ISO TC-120 work-piece.
The results show that the AE and vibration signal collected on the fixture connected to spindle housing can be used to detect the change of tool wear on a micro end mill and the alteration of different cutting path in milling. The tool wear monitoring system was developed by the vibration signal from the straight line milling detect the tool condition of line cutting path well. As the cutting path switched, the varied signal influence the decrease of system classification rate. In the tool wear classification results, the effect of system parameters such as bandwidth size of frequency domain signal and the length sizes of extracted signal could reduce the effect of cutting path. In consideration of the AE signal case, the influence of cutting path is slight. The feature selection of system development would not affected by the effect of cutting path. But each micro end mill has the different geometry of tool flute, the AE signals are easy varied by the different cutting tool. The variability would influence the tool wear monitoring system ability. In the tool wear classification results, the effect of system parameters such as bandwidth size of frequency domain signal and the length sizes of extracted signal could reduce the influence of the different geometry of tool flute as well.
|Appears in Collections:||機械工程學系所|
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