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Analysis of AE Signals and Sensor Locations between Turning and Milling Processes for Tool Wear Monitoring
|關鍵字:||聲射訊號;Acoustic emission signal;感測器安裝位置;單點與多點加工產生之影響;the location of sensor installation;single point cutting and multi-point cutting effect||出版社:||機械工程學系所||引用:|| K. Iwata, T. Moriwaki, 1977, "Application of acoustic emission measurement to in-process sensing of tool wear," Ann. CIRP 26(1-2) pp.19–23.  P. Grbec, P. Leskovar, 1977, "Acoustic emission of a cutting process," Ultrasonics, pp.17–20.  S. Liang, D. Dornfeld, 1989, "Tool wear detection using time series analysis of acoustic emission," J. Eng. Ind. Trans. ASME, pp.199–205.  M. R. Gorman, and W. H. Prosser, 1991, “AE source orientation by plate wave analysis,” Journal of Acoustic Emission, pp.283-288.  Xiaoli Li, 2002, “A brief review:acoustic emission method for tool wear monitoring during turning,” International Journal of Machine Tools and Manufacture, 42, pp.157-165.  M. Merchant, 1945, “Mechanics of the metal cutting process 1：orthogonal cutting and a type 2 chip,” Journal of Applied Physics, pp.267-275.  M. 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Hutton, F. Hu, 1999, "Acoustic Emission Monitoring of Tool Wear in End-Milling Using Time-Domain Averaging," Journal of Manufacturing Science and Engineering, pp.8-12.  C. C. Tan, 1990, "MONITORING OF TOOL WEAR USING ACOUSTIC EMISSION," Proceeding of the Tribology Conference, pp.1063-1067.  I. Tansel, M. Trujillo, 1998, "Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals," International Journal of Machine Tools & Manufacture, pp.1449–1466.  Pan Fu, 1998, "Intelligent Tool Condition Monitoring in milling operation," Southampton inst (united kingdom) systems engineering faculty, pp.1-10.  Mohammad Malekian, Simon S. Park, 2009, "Tool wear monitoring of micro-milling operations," Journal of Materials Processing Technology, pp.4903–4914.  XiaoQi C. , Hao Z. , Wildermuth D., 2001, "In-process tool Monitoring through acoustic emission sensing," Automated Material Processing Group, Automation Technology Division, pp.1-8.  Li Xiaoli, Yuan Zhejun, 1998, "Tool wear monitoring with wavelet packet transform-fuzzy clustering method," Wear, pp.145-154.  X Li, J Wu, 2000, "Wavelet Analysis of Acoustic Emission Signals in Boring," Proceedings of the Institution of Mechanical Engineers, pp.421-424.  Ding-Chen Chang, Satish Bukkapatnam, 2004, "Toward Characterizing the Microdynamics of AE Generation in Machining," Machining Science and Technology, pp. 235-261.  Chien-Wei Hung , Ming-Chyuan Lu, 2013, “Model development for tool wear effect on AE signal generation in micromilling,” International Journal of Advanced Manufacturing Technology, pp. 1845-1858  田仲凱，2011，車削刀具磨耗偵測之聲射訊號產生模型建立與分析，國立中興大學，碩士論文。||摘要:||
The acoustic emission (AE) signal can be used in the tool wear monitoring in machining. However, the change of cutting parameters, the location of sensor installation, and material will lead to the change of signal feature. To increase the understanding of AE signal generation, and improve the model for the relationship between tool wear and AE signal, the experimental study for the effect by the installation of sensor on tool and workpiece was conducted for single point cutting. At the same time, the AE signals obtained from single point cutting and multi-point cutting were analyzed as well.
In experimental setup, turning and boring process were conducted to simulate the signal point cutting process, as well as the milling process for multi-point cutting. Moreover, two sensors were installed on the top surface and side surface, respectively, for boring and milling experiments. The results show that the AE signals obtained from various location sensors, as well as cutting mode, show the same trend that the signal energy increases as tool wear proceeds. In addition, the distribution of frequency domain signal over frequency for all AE signals keep at the same pattern as tool wear increases. In investigating the AE signals obtained from sensors on tool and workpiece for single point cutting, the same pattern of frequency domain signal can be obtained for specific direction of sensor installation. Investigating the signals obtained from AE sensor on the workpiece, but different surfaces, or on the tool holder but different surfaces, shows the different pattern. In the turning of tube and rod, the AE signal obtained from the turning of rod demonstrates the higher energy and wilder range of energy distribution over frequency than the turning of tube. In analyzing the effect of the distance between the cutting point and sensor location, the decrease of signal energy as the distance increases can be confirmed. Finally, sensitivity of AE signals to the variation of cutting parameters can be confirmed to be lower than audible sound signals.
|Appears in Collections:||機械工程學系所|
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