Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2564
標題: 應用AE技術於微細鑽孔監測
Application Of AE Technique In Monitoring Of Micro Drilling
作者: 黃仲賢
Hsien, Huang-Chung
關鍵字: Online tool condition monitoring;線上刀具狀態監測;micro drilling;machine tools;Acoustic Emission;微細鑽削;工具機;聲射
出版社: 機械工程學系所
引用: 【1】 Tarng, Y. S. et. al., “ An Intelligent Sensor for Monitoring Milling Cutter Breakage,” Advanced Manufacturing Technology, Vol. 9, No. 3, 1994, pp. 141-146. 【2】 Koren Y., Ko T., and Galip A., “Flank wear estimation under varying cutting conditions,”J. Dyn. Syst., Meas., Contr., vol. 113, 1991, pp. 300–307. 【3】 Soliman E., and Ismail F., “Chatter Detection by Monitoring Spindle Drive Current,” Advanced Manufacturing Technology, 13, 1997, pp.27~34. 【4】 Mannan M. A., and Tomas N. ,“The behavior of static torque and thrust due to tool wear in drilling,”Tech. Rep., North Amer. Manufact. Res. Inst. SME, 1997. 【5】 陳金璋,“銑削刀具中刀具磨損監測與製程控制”,碩士論文,中興大學機械系,1996。 【6】 李誌慶,“刀具監測在車床上的應用”,碩士論文,中興大學機械系, 1994。 【7】 鄧文治,“線上刀具狀態監測之單晶化技術”,碩士論文,中興大學機械系,1997。 【8】 Li Xiaoli, Tso Shiu Kit,“Real-Time Tool Condition Monitoring Using Wavelet Transforms and Fuzzy Techniques,”Senior Member, IEEE, and Jun Wang, Senior Member , IEEE, vol.30, no.3, Aug. 2000, pp.352-357. 【9】 Li X., Yao Y., and Yuan Z.,“On-line tool condition monitoring system with wavelet fuzzy neural network,”J. Intell. Manufact., vol.8, Aug. 1997, pp.271-276. 【10】 Gong W., Obikawa T., and Shirakashi T.,“Monitoring of tool wear states in turning based on wavelet analysis,”JSME Int. J. Ser. Ⅲ-Vibr. Contr. Eng. Ind., vol. 40, Sept. 1997, pp. 447-453. 【11】 Song G. S., Rahman M., and Zhou Q.,“Using neural network for tool condition monitoring based on wavelet decomposition,”Int. J. Mach. Tools & Manufact., vol. 36, May 1996, pp.551-566. 【12】 Ya Wu R. Du,“Feature extraction and assessment using wavelet packets for monitoring of machining processes,”Mech. Syst. Signal Process., vol. 10, Jan. 1996, pp.29-53. 【13】 Zhou Q., Hong G. S., and Rahman M.,“New tool life criterion for toolcondition monitoring using a neural network,”Eng. Applicat. Artif. In-tell.,vol. 8, Oct. 1995, pp. 579–588. 【14】 Kasashima N., Mori K., Ruiz G. H., and Taniguchi N.,“On-line failuredetection in face milling using discrete wavelet transform,”Ann. CIRP,vol. 44, no. 1, 1995, pp. 483–487. 【15】 Tansel I.N., Mekdeci C., and McLaughlin C.,“Detection of tool failurein end milling with wavelet transformations and neural networks (WT-NN),”Int. J. Mach. Tools&Manufact., vol. 35, Aug. 1995, pp. 1137–1147. 【16】 Tanasel I.N., Mekdeci C., Rodriguez C., and Uragun O.,“Monitoring drill conditions with wavelet based encoding and neural networks,”Int.J. Mach. Tools Manufact., vol. 33, Aug. 1993, pp. 559–575. 【17】 Jemielniak K., “Some aspects of AE application in tool condition monitoring, ” Ultrasonics, 38, 2000, pp.604-608. 【18】 Dornfeld, D.A., 1992, “Application of acoustic emission techniques in manufacturing,” NDT&E International, Vol.25(6), pp.259-269. 【19】 Dornfeld, D.A., 1994, “In process recognition of cutting states,” JSME International Journal Series C: Dynamics Control, Vol.37(4), pp.638-650. 【20】 König, W., Kutzner, K. and Schehl, U., 1992, “Tool monitoring of small drills with acoustic emission,” International Journal of Machine Tools and Manufacture, Vol.32(4), pp.487-493. 【21】 Kakade S., Vijayaraghavan L., Krishnamurthy R., “In-process tool wear and chip-form monitoring in face milling operation using acoustic emission, ” Journal of Material Processing Technology, 44, 1994, pp.207–214. 【22】 Takata, S., et. al., “Tool Breakage Monitoring by Means of Fluctuations in Spindle Rotational Speed,” Annals of the CIRP, Vol. 36, 1987, pp.49~52. 【23】 Konig W., Kutzner K., Schehl U., “Tool monitoring of small drills with acoustic emission, ” International Journal of Machine Tools and Manufacture, 32 (4), 1992, pp.487–493. 【24】 Emel E., E. Kannatey-Asibu, “Tool failure monitoring in turning by pattern recognition analysis of AE signals, Trans. ASME, J. Eng. Ind. 110, 1988, pp.137-145. 【25】 Moriwaki T., Tobito M., “A new approach to automatic detection of life of coated tool based on acoustic emission measurement, ” ASME Trans. Journal of Engineering for Industry ,112 (3), 1990, pp.212–218. 【26】 Hayashi S. R., Thomas C. E., Wildes D. G., “Tool break detection by monitoring ultrasonic vibrations, ” Annals of the CIRP , 37 (1), 1988, pp.61–64. 【27】 Altintas, Y., et. al., “The Detection of Tool Breakage in Milling Operations,” Journal of Engineering for Industry, Vol.110, 1988, pp.271-277. 【28】 Tlusty J., Tarng Y. S., “Sensing cutter breakage in milling, ” Annals of the CIRP, 37 (1), 1988, pp.45–51. 【29】 Uhlman, W.T. ,and Schgmenk, M. J., “Torque Controlled Machine for Numerical Control Machining Center,” IEEE Paper, CH1707-9/81/0000-0055, 1981. 【30】 Elbestaw, M. A., “In-Process Monitoring of Tool Wear in Milling Using Cutting Force Signature,” Int. J. Mach. Tools Manufact., Vol. 31, No.1, 1991, pp.55-73. 【31】 Abdou, G., and Yien, J., “ Analysis of Force Patterns and Tool Lift in Milling Operations,” Advanced Manufacturing Technology, 10, 1995, pp.11-18. 【32】 Dimla. E., Dimla. Snr., “Sensor singal for tool-wear monitoring in metal cutting operation-a review of methods, ” International Journal of Machine Tool & Manufacturing, vol.40, 2000, pp.1073-1098. 【33】 Martin, K. F., “A Review by Discussion of Condition Monitoring and Fault Diagnosis in Machine Tools”, International Journal of Machine Tools & Manufacture, May 1994, pp.527-551. 【34】 彭毓霖,“銑削刀具磨耗監測回顧”,機械工業雜誌,1995, pp.177-183。 【35】 謝其懋,“刀具切削監測系統介紹與發展”,機械工業雜誌,1997,pp.253-266。 【36】 Novak, A. et. al., “On-Line Prediction of the Tool Life,” Annals of the CIRP, Vol.45, 1996, pp.93-96. 【37】 Javed, M. A. et. al. “On-Line Condition Monitoring Using Artificial Neural Network,” Insight, Vol. 38(5), 1996, pp. 351-354. 【38】 Jeong-Du KIM, In-Hyu CHOI, “Development of a tool failure detection system using multi-sensors, ” International Journal of Machine Tool & Manufacturing, Vol.36, No.8, 1996, pp.861-870. 【39】 Mannan M.A., Ashraf a. Kassin, Ma Jing, “Aplication of image and sound analysis techniques to monitor the condition of cutting tools, ” Pattern Recognition Letters, 21, 2000, pp.969-979. 【40】 Rangwala, S., and Dornfeld, D., “Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring,” Journal of Engineering for Industry, Vol. 112, 1990, pp.219-228. 【41】 林仁昭、林士傑,“利用類神經網路監測面銑削刀具之磨耗”,中國機械工程學會10th學術研討會論文集,1993,pp.107-116。 【42】 Lin S.C., Lin R.J., “Tool wear monitoring in face milling using force signals, ” WEAR, 198, 1996, pp.136-142. 【43】 趙之眉,湯銘權,蔡在亶,“金屬切削原理”,民77年2月,pp.293-294。 【44】 陳國亮,“微細孔加工技術與發展”,機械月刊,第二十六卷第五期,2000,pp.401-413。 【45】 謝其懋,“刀具切削監測系統介紹與發展”,機械工業雜誌,1997,pp.253~266。 【46】 Spanner, J. C., Brown, A. and Hay, D.R., V., Notvest, K. and Pollock, A. ,“Fundationals of Acoustic Emission Testing”, Nondestructive Testing Handbook, 2nd Ed., Vol. 5, 1987, pp.11-44. 【47】 Bray, Don, E. and McBride, “Acoustic Emission Technology”, Nondestructive Testing Techniques, , John Wiley & Sons Inc, 1992, pp.345-377. 【48】 Bassim, M. N. and Houssny-Emam, M., “Time and Frequency Analysisof Acoustic Emission Signals”, Nondestructive Testing Monographs andTracts Vol. 2:Acoustic Emission, 1983, pp.139-163. 【49】 曾聖智,“岩石材料之音射特性及音射源定位研究”,碩士論文,國立成功大學土木工程系,1999。 【50】 Guillem Quintana, Joaquim Ciurana, Daniel Teixidor, “A new experimental methodology for identification of stability lobes diagram in milling operations”, International Journal of Machine Tools & Manufacture, 2008, pp.1637-1645.
摘要: 
針對微細切削加工與傳統大尺寸切削之特性差異、所面臨的挑戰、以及理論模型建立與模擬方法都不盡相同,在傳統大尺寸中刀具監測常用的方式在微細切削中,因微細刀具磨耗造成之訊號變異較傳統刀具切削相對微弱,所取得訊號抗雜訊能力更差,造成切削力與功率等變化之量測更為困難。因此針對微細切削刀具細小特性,探討微細刀具狀態的發展方向。
本研究設備使用CIZITEN的Cincom R04走心式車床加工機,訊號擷取裝置為Kistler Type 8152B1的AE SENSOR,擷取介面採研華公司訊號擷取卡PCI-1714-UL,並使用LabVIEW開發人機介面。
本研究對此加工機所使用微細鑽孔刀具,針對其微細切削刀具細小特性,從刀具狀態監控系統與調整加工參數雙方面改進製程,達到提高產品良率與提高工具機使用率的目標。
在刀具狀態監控系統中,首先找出刀具斷裂時產生AE訊號的振幅,以此為參考,設定加工中AE訊號的上限值,當AE訊號達上限值時系統發出警報通知操作人員處理。並且改變加工參數實驗以田口方法,探討在改變加工參數下可以利用此方法,找出加工速度與刀具損耗的最佳組合條件,以此提升工具機使用效率,或是在不同情況下使用不同的生產速度。
以本研究中所使用的刀具狀態監控系統在工廠中使用可以減少因刀具損壞造成的產品不良率,並且減少人力資源在檢驗產品區分良與不良品。使用田口法找出加工速度與刀具損壞的平衡點,以交貨期長短可以使用不同生產效率。

For micro-machining and cutting characteristics of the traditional large size difference, the challenges faced, and the theoretical modeling and simulation methods are different, the large size of the traditional tools used to monitor the way in micro cutting, the tool wear caused by fine The signal variation is relatively weak compared with conventional cutting tool, made even worse signal noise immunity, resulting in changes of cutting force and power and so the measurement more difficult. Therefore, micro-cutting tools for small features, explore the direction of development of micro-tool condition.
This research instrument uses CIZITEN Cincom R04 to be distracted the Swiss type lathe, signal acquisition device for Kistler Type 8152B1 of AE SENSOR, capture interface mining Advantech signal acquisition card PCI-1714-UL, and use the LabVIEW development of interface.
This study used this machine for micro drilling tools, small cutting tools for its fine features, from the tool condition monitoring system and the adjustment of processing parameters to improve two-way process, to improve product yields and improve machine tool utilization goals.
In tool condition monitoring system, first identify tool fracture generated AE signal amplitude, as a reference, set the AE signal processing in the upper limit, when the AE signal when the upper level system alerts the operator to inform treatment. And change the processing parameters to the Taguchi experimental method of changing the processing parameters can use this method, processing speed and tool to find the best combination of wear and tear conditions, in order to enhance the efficiency of machine tool use, or in different situations using different production rate.
To this study, used in tool condition monitoring system in the factory can reduce the damage caused by product defect rate, and to reduce the human resources in checking good or defective products. Using the Taguchi method to find the processing speed and tool damage the balance to the length of delivery time can use different production efficiency.
URI: http://hdl.handle.net/11455/2564
其他識別: U0005-2907201014523200
Appears in Collections:機械工程學系所

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