Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2258
標題: 整合聲音訊號與自組特徵映射網路於微細刀具磨耗狀態之應用研究
Study of sound based tool wear monitoring in micro milling using Self-Organizing Map
作者: 李明興
Lee, Ming-Hsing
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
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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. [18] 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. [19] 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. [20] 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. [21] 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. [22] 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. [23] Wavelet Toolbox User''s Guide, 2008, http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavelet/wavelet_ug.pdf. [24] 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. [25] 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. [26] 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. [27] 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. [28] 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. [29] Haykin, S., Van Veen, B., 2003, Signals and System, John Wiley & Sons. [30] Schilling, R. J. and Harris, S. L., 2005, Fundamentals of Digital Signal Processing Using MATLAB, Thomson. [31] 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. [32] Kohonen, T., 1982, “Self-Organized formation of topologically correct feature maps,” Biological Cybernetics, Vol.43, pp.59-69. [33] 張斐章、張麗秋,2005,類神經網路,東華書局。 [34] Neural Network Toolbox User''s Guide, 2009, http://www.mathworks.com/access/helpdesk/help/pdf_doc/nnet/nnet.pdf [35] 葉怡成,1993,類神經網路模式應用與實作,儒林圖書公司。 [36] Kohonen, T., 1986, Learning Vector Quantization for Pattern Recognition, Technical Report TKK-F-A601, Helsinki University of Technology, Finland. [37] Bishop, C. M., 2006, Pattern Recognition And Machine Learning, Springer. [38] 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: 
近年來隨著科技不斷地進步,其生產出來之產品,也朝向輕薄短小,因此微細切削加工之發展有其必要性。但於微細切削加工過程中,刀具極易磨耗,而刀具磨耗對產品精度之影響也較傳統尺寸大,因此刀具狀態偵測系統之發展更形重要。
本文目的在於建構聲音訊號於微細刀具磨耗狀態偵測系統,係利用麥克風感測器擷取加工過程中聲音變化之訊號,透過快速傅立葉轉換取得頻域訊號之能量分佈並經過群組分離準則以及自組性特徵映射神經網路作為特徵資料之處理。實驗刀具為直徑700 之微細銑刀,工件材料採用SK2高碳鋼,分類器設計上係以學習向量量化與費雪線性區分法做為辨識分類之用。
實驗結果顯示,隨著頻帶寬度增加,能降低聲音訊號之雜訊,其辨識成功率亦隨之增加。當頻帶寬度達到飽和時,與磨耗相關之聲音訊號亦隨之平均而減弱。透過群組分離準則找出與刀具狀態變化最具相關之頻帶能量,輸入至自組特徵映射網路模型處理訊號特徵再輸出至分類器。結果顯示在頻帶寬度8KHz時有相對較高之刀具狀態差異訊號,經由學習向量量化與費雪線性區分之分類器,對於銳刀與鈍刀辨識成功率皆能達到100%。

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.
URI: http://hdl.handle.net/11455/2258
其他識別: U0005-1608200913001100
Appears in Collections:機械工程學系所

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