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Study of sound based tool wear monitoring in micro milling using Self-Organizing Map
|關鍵字:||Micro cutting tool;微細刀具;Sound signal;SOM;聲音訊號;自組特徵映射網路||出版社:||機械工程學系所||引用:|| Chae, J., Park, S. S., Freiheit, T., 2006, ‘‘Investigation of micro-cutting operations,” International Journal of Machine Tools & Manufacture, Vol.46, pp.313-332  Shaw, M. C., 2005, Metal Cutting Principles, Oxford.  Lin, Z. Q., Ai, X., Zhang, H., Wang, Z. T., Wan, Y., 2002, ‘‘Wear patterns and mechanisms of cutting tools in high-speed face milling,” Journal of Materials Processing Technology, Vol.129, pp.222-226  Boothroyd, G., 1976, Fundamentals of metal machining and machine tools, McGraw-Hill.  Dimla, E. D., 2000, “Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods,” International Journal of Machine Tools and Manufacture, Vol.40(8), pp.1073-1098.  Teti, R., 1995, ‘‘A review of tool condition monitoring literature database,” Annals of the CIRP, Vol.44(2), pp.659-666.  Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., Konig, W. and Teti, R., 1995, “Tool condition monitoring (TCM)-the status of research and industrial application,” Annals of CIRP ,Vol.44(2), pp.541-567.  S.Kurada, C.Bradley, 1997, “A review of machine vision sensors for tool condition monitoring,” Computers in Industry, Vol.34, pp.55-72.  Wang, W. H., Hong, G.. S., Wong, Y. S., Zhu, K. P., 2007, “Sensor fusion for online tool condition monitoring in milling, ‘‘International journal of Production Reseach, Vol.45(21), pp.5095-5116.  Dornfeld, D.A., 1992, “Application of acoustic emission techniques in manufacturing,” NDT&E International, Vol.25(6), pp.259-269.  Dornfeld, D.A., 1994, “In process recognition of cutting states,” JSME International Journal Series C: Dynamics Control, Vol.37(4), pp. 638-650.  Kakade, S., Vijayaraghavan, L. and Krishnamurthy, R., 1994, “In-process tool wear and chip-form monitoring in face milling operation using acoustic emission,” Journal of Material Processing Technology, Vol.44, pp.207-214.  顏嘉良，2008，應用類神經網路於微細切削刀具狀態偵測之研究，國立中興大學機械所，碩士論文。  Ravindra, H. V., Srinivasa, Y. G. and Krishnamurthy, R., 1993, ‘‘Modelling of tool wear based on cutting forces in turning,” Wear, Vol.169, pp.25-32.  El-Wardany, T. I., Gao, D. and Elbestawi, M. A.,1996, “Tool condition monitoring in drilling using vibration signature analysis,” International Journal of Machine Tools and Manufacture, Vol.36(6), pp.687-711.  Sadat, A. B., and Raman, S., 1987, ‘‘Detection of Tool Flank Wear Using Acoustic Signature Analysis,’’ Wear, Vol.115, pp.265-272.  Lu, M. C. and Kannatey-Asibu, E. Jr., 2002, ‘‘Analysis of sound signal generation due to flank wear in turning,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, Vol.124(4), pp. 799-808.  Lu, M. C. and Kannatey-Asibu, E. Jr., 2004, ‘‘Flank wear and process characteristic effect on system dynamics in turning,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, Vol. 126(1), pp.131-140.  Lin, J., 1995, ‘‘Inverse estimation of the tool-work interface temperature in end milling,” International Journal of Machine Tools and Manufacture , Vol.355, pp.751-760.  Quan, Y., Zhou, M., Luo, Z., 1998, ‘‘On-line robust identification of tool-wear via multi-sensor neural-network fusion,” Egineering Applications of Artificial Intelligence, Vol.11, pp. 717-722.  Ghosh, N., Raci, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., Chattopadhyay, A. B., 2007, ‘‘Estimation of tool wear during CNC milling using neural network-based sensor fusion,” Mechanical Systems and Signal Processing, Vol.21, pp.466-479.  Kandilli, I., Sonmez, M., Ertunc, H.M., Cakir, B., 2007, ‘‘Online monitoring of tool wear in drilling and milling by multi-sensor neural network fusion,” International Conference on Mechatronics and Automation, pp.1388-1394.  Wavelet Toolbox User''s Guide, 2008, http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavelet/wavelet_ug.pdf.  Sick, B., 2002, ‘‘On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research,” Mechanical Systems and Signal Processing, Vol.16(4), pp.487-546.  Yao, Y., Li, X. and Yuan, Z., 1999, ‘‘Tool wear detection with fuzzy classification and wavelet fuzzy neural network,” International Journal of Machine Tools and Manufacture, Vol.39(10), pp.1525-1538.  Li, X. L., Tso, S. K., 2000, ‘‘Real-time tool condition monitoring using wavelet transforms and fuzzy techniques, ‘‘ Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol.30(3), pp.352-357.  Kannatey-Asibu, E. Jr. and Emel, E., 1987, ‘‘Linear discriminant function analysis of acoustic emission signals for cutting tool monitoring,” Mechanical Systems and Signal Processing, Vol.1(4), pp.333-347.  Trabeisi, H. and Kannatey-Asibu, E. Jr., 1991, ‘‘Pattern-recognition analysis of sound radiation in metal cutting,” The International Journal of Advanced Manufacturing Technology, Vol.6, pp.220-231.  Haykin, S., Van Veen, B., 2003, Signals and System, John Wiley & Sons.  Schilling, R. J. and Harris, S. L., 2005, Fundamentals of Digital Signal Processing Using MATLAB, Thomson.  Yen, C. L., Lu, M. C., Lin, C. Y., and Chen, T. H., 2008, “Draft-Experimental Study of sound signal for tool condition monitoring in micro milling processes,” Proceedings of the 2008 International Manufacturing Science And Engineering Conference.  Kohonen, T., 1982, “Self-Organized formation of topologically correct feature maps,” Biological Cybernetics, Vol.43, pp.59-69.  張斐章、張麗秋，2005，類神經網路，東華書局。  Neural Network Toolbox User''s Guide, 2009, http://www.mathworks.com/access/helpdesk/help/pdf_doc/nnet/nnet.pdf  葉怡成，1993，類神經網路模式應用與實作，儒林圖書公司。  Kohonen, T., 1986, Learning Vector Quantization for Pattern Recognition, Technical Report TKK-F-A601, Helsinki University of Technology, Finland.  Bishop, C. M., 2006, Pattern Recognition And Machine Learning, Springer.  協銳精密工業股份有限公司，2007，超硬切削工具型號目錄。||摘要:||
With the fast development in technology. The demand of the product is getting smaller and smaller. Therefore, the development of micro-cutting technology draws a much more attention. In the micro-cutting process, the effect of tool wear on the product quality is much more serious than in the conventional cutting. It is necessary to establish tool condition monitoring.
In this reserch, the purpose to establish tool wear monitoring system for the micro milling was based on the sound signal obtained by the microphone sensor in the cutting process. The way of feature signal processing was using fast Fourier Transform to get frequency spectrum. After class scatter criterion, the feature signal was putting on self-organizing Map to reducing variance. The experiment was setup with SK2 workpiece milled by the micro endmill of 700 in diameter. For the classifier design. Learning Vector Quantization(LVQ) network and Fisher Linear Discriminant was used to classify the tool condition.
The result shows that the performance of classification was getting batter withing the increasing bandwidth size of feature reducing noise of sound signal. After saturation of increasing bandwidth size of feature the sound signal corresponding with tool wear condition was getting weaker. Putting the chosen features into the LVQ and FLD classifiers. It shows that when bandwidth size of feature reaching 8KHz, the performance for sharp and worn tool testing were 100% probability of classification.
|Appears in Collections:||機械工程學系所|
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