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dc.contributor.authorHsieh, Wan-Haoen_US
dc.identifier.citation[1] Kwak, J.S., 2006, “Application of Wavelet Transform Technique Tool Failure in Turning Operations,” Int J Adv Manuf Technol 28,1078-1083 [2] 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 15,313-318. [3]Li, X., 1999 ”On-line Detection of the Breakage of Small Diameter Drills using Current Signature Wavelet Transform,” International Journal of Machine Tools & Manufacture 39,157-164 [4]Li, P.Y., Fang, Y.W., Wang, Y., Yang, M.S., Yuan, Q.L. and Li,Y., 2006, “Time-Frequency Analysis For Cutting Tools Wear Characteristics, ”Proceeding of the Fifth International Conference on Machine Learning and Cybernetics,Dalian,13-16 [5]Elijah, K.A. and Erdal, E., 1987,”Linear Discriminant Function Analysis of Acoustic Emission Signals for Cutting Tool Monitoring,” Mechanical Systems and Signal Processing 1, 333-347. [6]Cus, F. and Zuperl, U., 2005, “Approach to Optimization of Cutting Conditions by using Artificial Neural Networks.” Journal of Materials Processing Technology 173, 281-290. [7]Issam, A.M., 2003,” Drilling Wear Detection and Classification using Vibration Signals and Artificial Neural Network,” International Journal of Machine Tools & Manufacture 43 , 707-720. [8]Patra, K., Pal, S.K. and Bhattacharyya, K., 2006,”Drill Wear Monitoring through Current Signature Analysis using Wavelet Packet Transform and Artifical Neural Network,” Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, India. [9]Yan, J. and Lee, J., 2007, “A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operation,” Proceedings of the IEEE,International Conference on Automation and Logistics August 18-21,Jinan,China. [10]Pai, P.S., Nagabhushana T.N. and Rao, P.K.R., 2002, ”Flank wear Estimation in Face Milling Based on Radial Basis Function Neural Networks,” Int J Adv Manuf Technol 20,241-247 [11] Tansel, I. N., Arkan, T. T., Bao, W. Y., Mahendrakar, N., Shisler, B., Smith, D. and McCool, M., 1999 “Tool Wear Estimation in Micro-Machining. Part I: Tool Usage-Cutting Force Relationship.” International Journal of Machine Tools & Manufacture 40, 599-608. [12] Tansel, I. N., Arkan, T. T., Bao, W. Y., Mahendrakar, N., Shisler, B., Smith, D. and McCool, M., 1999 “Tool Wear Estimation in Micro-Machining. Part II: Neural-Network-Based Periodic Inspector.” International Journal of Machine Tools & Manufacture 40, 609-620. [13] Obikawa, T. and Shinozuka, J. , 2004, “Monitoring of Flank Wear of Coated Tools in High Speed Machining with a Neural Network ART2,”International Journal of Machine Tools & Manufacture 44,1311-1318 [14] Matlab, 2008,Wavelet Toolbox User’s Guide [15]楊福生,小波變換的工程分析與應用,科學出版社,北京 [16] Daubechies, I. ,1990,” The Trandform, Time-Frequency Localization and Signal Analysis.”IEEE Transation on Information Theory ,vot.36,NO.5 [17] 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,碩士論文,中興大學機械工程研究所 [18] 陳柏元,2005,應用小波轉換及人工智慧進行配電系統電容切換暫態位置之判斷,碩士論文,中原大學電機工程研究所 [19] 羅明哲,2001,小波轉換於轉子故障與切削顫振偵測之應用,碩士論文,中原大學機械工程研究所zh_TW
dc.description.abstract世界之製造產業大多仰賴工具機生產其產品,工具機在硬體方面以達相當水準,軟體方面需更進一步開發達到高精密工具機等級,故開發工具機相關軟體技術為提升產品性能與附加價值為必要的手段。其中,微系刀具之真測可提升加工品質之穩定 本研究開發微銑刀具狀態偵測系統包含切削平台、量測模組、訊號轉換、特徵處理與分類器,以外掛於主軸之三軸加速規量測切削過程中因刀具狀態改變之振動訊號變化。所取得的振動訊號,分別採用小波轉換應用判斷刀具斷裂偵測,快速傅利葉轉換應用於刀具磨耗偵測,在特徵選取則以群組分離準則選取特徵,分類器使用倒傳遞類神經法則。 系統開發之實驗刀具為700 m直徑之微銑刀,工件材料為SK2高碳鋼;刀具斷裂實驗結果顯示,振動訊號利用小波轉換與小波包轉換對於系統判斷刀具斷裂成功率皆有相同之水準,因小波轉換所需之運算量較少,故小波轉換應用於判斷振動與刀具狀態之關係為最佳之訊號轉換方式。磨耗實驗中,在相同頻寬30Hz,振動訊號經群組分離準則取5項可使系統準確判斷刀具狀態,擷取4項與3項特徵無法使系統準確判斷刀具狀態,在相同頻寬60Hz,振動訊號經群組分離準則取5項與4項特徵皆保持系統相同判斷水準,其擷取3項特徵可提高系統判斷刀具狀態之成功率,最後輸入雙方向振動訊號之特徵可改善系統準確判斷刀具狀態。zh_TW
dc.description.abstractThe development of sensing and monitoring system plays an important role in improving the accuracy and stability for machine tool. The micro tool condition monitoring system integrated by sensor system, signal transformation, feature selection, and classifier was developed in this study. A three axis accelerometer was installed on the sensor plate, which fixed on the spindle housing, to collect the vibration signal in the cutting processes. The Wavelet and FFT transformation were used for transforming the time domain signal to the other domains to modify and select the features for tool breakage and tool wear monitoring, respectively. For classification, the back propagation neural network was designed to classify the tool condition. In order collect the date for training and verifying the system, an experiment was implemented along with 700 m diameter micro mill and SK2 workpiece. The results show that the transformation of vibration signal both by Digital Wavelet and Wavelet Package Transformation provides the same performance for classifying the tool breakage condition. In tool wear monitoring test, selecting 5 features for classification provides a better classification rate than selecting 4 and 3 features in the 30Hz bandwidth feature case. However, selecting 5 and 4 features all provide the better classification rate than 3 features with 60Hz bandwidth features selected. Finally, the multi sensor system was observed to show the better classification performance than only considering signal from a signal sensor.en_US
dc.description.tableofcontents摘要 i ABSTRACT ii 目錄 iii 圖目錄 v 表目錄 vii 第一章、緒論 1 1.1 前言 1 1.2 文獻回顧 2 1.3 研究目的與內容 3 第二章、訊號處理與分類器設計 5 2.1 傅立葉轉換(Fourier Transform) 5 2.2 小波轉換(Wavelet Transform) 5 2.3 特徵擷取 11 2.3.1 群組分離準則 11 2.4 類神經網路 13 2.4.1 生物神經元模型 13 2.4.2 導傳遞類神經網路 17 2.4.3 網路架構與演算法則 17 2.4.4 可變學習速率倒傳遞演算法 22 第三章、實驗設計與實驗結果討論 24 3.1 實驗設備與規劃 24 3.2 刀具斷裂實驗結果與討論 29 3.3 刀具磨耗實驗結果與討論 37 第四章、結論與未來展望 49 4.1 結論 49 4.2 未來展望 49 參考文獻 50zh_TW
dc.subjectMicro cuttingen_US
dc.subjectNeural Networken_US
dc.subjectTool monitoring systemen_US
dc.titleDevelopment of tool wear monitoring system in the micro milling using vibration signal and Neural Networken_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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