Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2939
標題: 麥克風陣列排列模式在微銑削刀具磨耗偵測之影響分析
Analysis of Orientation Effect for Microphone Array on Sound Based Tool Wear Monitoring System in Micro Milling
作者: 呂長恩
Lu, Chang-En
關鍵字: 刀具偵測;tool monitoring;波束成型法;偉納濾波;beamforming;Wiener filter
出版社: 機械工程學系所
引用: [1] Kurada, S., Bradley, C., 1997, “A Review Of Machine Vision Sensors For Tool Condition Monitoring,” Computers in Industry , v 34( 1), pp.55-72 [2] 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, v40(8), pp.1073-1098 [3] Jantunen, E., 2002, “A summary of methods applied to tool condition monitoring in drilling,” International Journal of Machine Tools & Manufacture, v.42, pp.997-1010 [4] Rehorn, A. G., Jiang, Jin, Orban, Peter E., 2005, “State-of-the-art methods and results in tool condition monitoring: A review,” International Journal of Advanced Manufacturing Technology, v 26( 7-8), pp.693-710 [5] Chae, J., Park, S. S., Freiheit, T., 2006, “Investigation of micro-cutting operations,” International Journal of Machine Tools & Manufacture, v46(3-4), pp.313–332 [6] Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., Konig, W., Teti, R., 1995, “Tool Condition Monitoring (TCM) - The Status of Research and Industrial Application,” CIRP Annals - Manufacturing Technology, v 44, Issue 2, pp.541-567 [7] El-Wardany, T. I., Gao, D., Elbestawi, M. A., 1996, “Tool condition monitoring in drilling using vibration signature analysis,” International Journal of Machine Tools and Manufacture, v36(6), pp.687-711 [8] Dornfeld, D. A., 1992, “Application of acoustic emission techniques in manufacturing,” NDT&E International, v25(6), pp.259-269 [9] Kakade, S., Vijayaraghavan, L., Krishnamurthy, R., 1994, “In-process tool wear and chip-form monitoring in face milling operation using acoustic emission,” Journal of Material Processing Technology, v44, pp.207-214 [10] Ravindra, H. V., Srinivasa, Y. G., Krishnamurthy, R., 1993, “Modelling of tool wear based on cutting forces in turning,” Wear, v169, pp.25-32 [11] Tarng, Y. S. and Lee, B. Y., 1999, “Amplitude demodulation of the induction motor current for the tool breakage detection in drilling operations,” Robotics and Computer Integrated Manufacturing, v15, pp.313-318 [12] Weller, E. J., Schrier, H. M., Weichbrodt, B., 1969, “What Sound Can Be Expected From A Worn Tool ?,” Trans. ASME, J. Engng for Industry, v91(3), pp.525-534 [13] Sadat, A. B. and Raman, S., 1987, “Detection of Tool Flank Wear Using Acoustic Signature Analysis,” Wear , v115(3), pp.265-272 [14] Mannan, M. A., Kassim, Ashraf A., Jing, Ma., 2000, “Application of image and sound analysis techniques to monitor the condition of cutting tools,” Pattern Recognition Letters, v 21(11), pp. 969-979 [15] Trabelsi, H. and Kannatey-Asibu, Jr, E., 1991, “Pattern recognition Analysis of Sound Radiation In Metal Cutting,” Int. J. Adv. Mfg Technol., v6, pp.220-231 [16] Kopac, J. and Sali, S., 2001, “Tool Wear Monitoring During The Turning Process,” J. Mater. Process. Technol., v113(1-3), pp.312–316 [17] 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, v124(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, v126(1), pp.131-140 [19] Tekiner, Z., Yesilyurt, S., 2004, “Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel,” Materials and Design, v 25(6), pp.507-513 [20] Alonso, F. J., Salgado, D. R., 2005, “Application of singular spectrum analysis to tool wear detection using sound signals,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, v219(9), pp.703-710 [21] Salgado, D. R., Alonso, F. J., 2007, “An approach based on current and sound signals for in-process tool wear monitoring,” International Journal of Machine Tools and Manufacture, v 47( 14), pp.2140-2152 [22] 萬秉勳,2010,隱藏式馬可夫模型應用於微細刀具磨耗狀態偵測之研究,國立中興大學碩士論文 [23] Elko, G. W., 1996 , “Microphone array systems for hands-free telecommunication, ” Speech Communication, v20(3-4), pp.229-240 [24] Krim, H., Viberg, M., 1996, “Two decades of array signal processing research ,” IEEE Signal Processing Magazine, v13(4), pp.67-93 [25] Dedieu, S., Moquin, P. and Goubran, R., 2005, “Sound Measurement in Noisy Environment Using Optimized Conformal Microphone Arrays,” Instrumentation and Measurement Technology Conference, IMTC’05. Proceedings of the IEEE, v1, pp.748 - 751 [26] 賴建端、簡仁宗,2000,結合麥克風陣列及模型調整技術之遠距離語音辨識系統,國立成功大學 [27] Fischer, S., Simmer, K. U., 1996, “Beamforming microphone arrays for speech acquisition in noisy environments,” Speech Communication, v20(3-4), pp.215-227. [28] Vaseghi, S. V., 1996, “Advanced Signal Processing and Digital Noise Reduction,” John Wiley&Sons and B.G Teubner Publishers [29] Mahmoudi, D., 1997, “A Microphone Array for Speech Enhancement Using Multiresolution Wavelet Transform,” in Proc. of Eurospeech97, pp.339-342 [30] Kobayashi, K., Furuya, K., Kataoka, A., 2006, “A microphone array system with echo canceller,” Electronics and Communications in Japan (Part III: Fundamental Electronic Science), v89(10), pp.23-31 [31] Zelinski, R., 1988, “A microphone array with adaptive post-filtering for noise reduction in reverberant rooms,” Proceedings of the IEEE, pp.2578-2581 [32] Gazor, S., Grenier, Y., 1995, “Criteria for positioning of sensors for a microphone array,” Proceedings of the IEEE, v3(4), pp. 294-303 [33] 蔡國隆、王光賢、涂聰賢,“聲學原理與噪音量測控制”,全華科技圖書股份有限公司 [34] 黃集豐,2009,麥克風陣列在刀具磨耗偵測之應用研究,國立中興大學碩士論文 [35] Barry D. Van Veen and Kevin M. Buckley, 1988, “Beamforming:a versatile approach to spatial filtering,” Proceedings of the IEEE, pp. 4-24 [36] Johnson, D. H., Dudgeon, D. E., 1993, “Array Signal processing : concepts and techniques,” P T R Prentice Hall,Englewood Cliffs [37] Wavelet Toolbox User''s Guide, 2008, http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavelet/wavelet_ug.pdf [38] Cooley, J. W., Tukey, J. W., 1965, “An Algorithm for the Machine Calculation of Complex Fourier Series,” Math. Computat., v19, pp.297-301 [39] Schilling, R. J., Harris, S. L., 2005, “Fundamentals of Digital SignalProcessing Using MATLAB, ” Thomson. [40] Haykin, S., Van Veen, B., 2003, “ Signals and System,” John Wiley & Sons [41] 李明興,2009,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,國立中興大學碩士論文 [42] Bishop, C. M., 2006, Pattern Recognition And Machine Learning, Springer [43] 協銳精密工業股份有限公司,2007,超硬切削工具型號目錄 [44] 黃耀賢,2010,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
摘要: 
隨著科技不斷的進步,日常生活中各種產品逐漸朝向小體積、高精度以及節能等方向發展,由於零件與結構的微型化可以提昇產品效能及縮小配置空間,因此如何使零件與結構微型化慢慢成為新的技術發展方向,而微細加工技術在微型技術發展過程中佔有相當重要的地位。在切削加工過程中,刀具的狀態對於整體切削的品質與成本的影響很大,所以開發刀具狀態偵測系統有其必要性。由於在切削加工過程中,經驗豐富的技師常能藉由切削的聲音得知刀具的狀態,然而應用聲音建構自動化刀具狀態偵測系統容易因背景噪音的干擾而造成系統之誤動作,因此為了提升偵測系統的穩定性,抑制噪音技術扮演著重要的角色。
本研究探討環狀排列麥克風陣列在降低干擾噪音之效能,以及對於刀具磨耗偵測系統的影響。實驗過程採用直徑700μm之微細銑刀,工件為SK2 高碳鋼,並在切削過程以喇叭提供人工噪音源,並利用麥克風陣列擷取切削時的聲音訊號。擷取之聲音訊號接續以單支麥克風偉納濾波系統、麥克風陣列濾波系統,以及整合陣列與偉納濾波系統分別進行濾波處理,分析不同系統之噪音濾除效能。在分析各濾波系統對刀具磨耗辨識之性能影響方面,結果顯示整合環列麥克風陣列與偉納濾波系統較其他濾波系統能更有效的去除雜訊,達到100%的辨識成功率。此外,使用高頻麥克風所接收之聲音訊號,因其於高頻部份的訊號不易受到外在干擾噪音的影響,在高能量噪音之干擾之下,雖無導入噪音去除系統,系統還能有效的偵測刀具磨秏之狀態。

Micro machining draws a much attention lately due to the increasing need in miniaturized devices. At the same time, the tool condition monitoring plays an important role in improving the cutting quality and efficiency in micro-mechanical machining. In tradition, an experienced technician can detect the tool condition easily by the sound generated during cutting. However, the sound signal contaminated by the background noise will make the sound based monitoring system not reliable as expected. Therefore, how to reduce the noise effect play a crucial role in developing the sound based tool condition monitoring system for practical applications.
This research focus on the study of the capability of microphone array on the noise reduction and the improvement of sound based tool wear monitoring. Two arrangements for the installation of microphone arrays were discussed in this thesis. In experimental setup, 700um micro end mill was used in cutting SK2 workpiece. To simulate the noise occurring during factory, the speaker was installed inside the cutting chamber to generate the broad band noise during cutting. After sound signals were collected by microphone during cutting with various tool wear conditions, they were processed by system with the microphone array filter, the Wiener filter, and the combination of the microphone array filter and the Wiener filter, respectively. The results show that 100% classification rate can be obtained by processing the sound signal with the combination of microphone array and the Wiener filter. In the study of adopting the high frequency microphone with bandwidth up to 80 kHz, the results show that the system without the implementation of noise reduction algorithm still can detect the tool wear effectively due to the features higher than 20 kHz was not deteriorated by the generated noise.
URI: http://hdl.handle.net/11455/2939
其他識別: U0005-2308201223213700
Appears in Collections:機械工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.