Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2919
標題: 車削與銑削刀具磨耗偵測之聲射感應器位置與訊號比較分析
Analysis of AE Signals and Sensor Locations between Turning and Milling Processes for Tool Wear Monitoring
作者: 施瀚博
Shih, Han-Po
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
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Kannatey-Asibu, and D. A. Dornfeld, 1981, “Quantitative relationship for acoustic emission from orthogonal metal cutting,” Journal of Engineering for Industry, pp.330-340. [9] E. Kannatey-Asibu, and D. A. Dornfeld, 1982, “A study of tool wear using statistical analysis of metal cutting acoustic emission,” Wear, pp.247-261. [10] T. Blum and I. Inasaki, 1990, "Study on acoustic emission from the orthogonal cutting process," Journal of engineering for industry, pp.203-211. [11] Slavko. Dolinsek , Janez Kopac, 1999, "Acoustic emission signals for tool wear identification," Wear, pp. 295–303. [12] M. T. Telsang, Katu, 2004, "Condition Monitoring of Single-Point Cutting Tool Using Acoustic Emission Technique," Journal of the Institution of Engineers(India), pp.77-81. [13] Chungchoo C, Saini D, 2002, "A computer algorithm for flank and crater wear estimation in CNC turning operations," Int J Mach Tool Manuf 42, pp.1465-1477. [14] A. Al-Habaibeh, A.Al-Azmi, 2010, "The Application of Force and Acoustic Emission Sensors for Detecting Tool Damage in Turning Processes," Key Engineering Materials, pp.381-384. [15] Ichiro Inasaki, 1998, "Application of acoustic emission sensor for monitoring machining processes," Ultrasonics, pp.273-281. [16] Rangwala, Dornfeld, 1990, "Sensor integration using neural networks for intelligent tool condition monitoring," Trans ASME J Eng, pp.219-228. [17] I. Inasaki, S. Yonetsu, 1981, "In-process detection of cutting tool damage by acoustic emission measurement," Machine tool design and Research, pp.261-268. [18] E. M. Rubio, R. Teti, 2006, "Advanced signal processing in acoustic emission monitoring systems for machining technology," Intelligent Production Machines and Systems, pp.1-6. [19] R. X. Gao, C. R. Friedrich, 1994, "Acoustic Emission Measurement for the in-Process Monitoring of Diamond Turning," IEEE Instrumentation and Measurement Technology Conference, pp.757-760. [20] Dimla E. , Dimla Snr. , 2000, "Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods," International Journal of Machine Tools & Manufacture, pp.1073–1098. [21] Li Dan, J. Mathew, 1990, "Tool wear and failure monitoring techniques for turning-A review," Int. Mach. Tools manuf, pp.579-598. [22] Jemielniak K, Urbański T, Kossakowska J, Bombiński S, 2012, "Tool condition monitoring based on numerous signal features," Int J Adv Manuf Technol, pp.73–81. [23] P. Srinivasa Pai, 2002, "Acoustic emission analysis for tool wear monitoring in face milling," International Journal of Production Research, pp.1081-1093. [24] R. H. Osuri, S. Chatterjee, 1991, "On-line condition monitoring of tool wear in end milling using acoustic emission," International Journal of Production Research, pp. 1339-1353. [25] D. V. 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. [26] C. C. Tan, 1990, "MONITORING OF TOOL WEAR USING ACOUSTIC EMISSION," Proceeding of the Tribology Conference, pp.1063-1067. [27] 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. [28] Pan Fu, 1998, "Intelligent Tool Condition Monitoring in milling operation," Southampton inst (united kingdom) systems engineering faculty, pp.1-10. [29] Mohammad Malekian, Simon S. Park, 2009, "Tool wear monitoring of micro-milling operations," Journal of Materials Processing Technology, pp.4903–4914. [30] 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. [31] Li Xiaoli, Yuan Zhejun, 1998, "Tool wear monitoring with wavelet packet transform-fuzzy clustering method," Wear, pp.145-154. [32] X Li, J Wu, 2000, "Wavelet Analysis of Acoustic Emission Signals in Boring," Proceedings of the Institution of Mechanical Engineers, pp.421-424. [33] Ding-Chen Chang, Satish Bukkapatnam, 2004, "Toward Characterizing the Microdynamics of AE Generation in Machining," Machining Science and Technology, pp. 235-261. [34] 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 [35] 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: 
在切削加工時,可利用聲射感測器監測刀具磨耗狀態,但訊號常因加工參數、感應器安裝位置或材料變異而改變。為了增加對加工聲射訊號產生機制以及刀具磨耗對聲射訊號影響之了解,並改善加工中聲射訊號產生之模擬正確性,本研究實驗探討在單點加工模式下,感應器安裝於刀具端與工件端之訊號差異;另一方面,分析研究加工過程中單點加工與多點加工模式加工對聲射訊號產生之影響。 實驗中,將分別以外徑車削與搪孔實驗模擬單點加工狀態,且將感應器分別安裝於刀把上以及工件上,同時間以銑削加工模擬多點加工之狀態。另外,於搪孔加工與銑削加工中同時安裝感應器於工件上不同面之位置。
實驗結果所示,各個實驗中不同位置感應器所獲得的訊號都有因刀腹磨耗增加而能量增大的趨勢,且隨著刀腹磨耗增加,頻域訊號於固定頻率上之能量增大,但並無訊號分布偏移之現象。觀察相同單點切削模式下不同位置的感測器訊號,在特定之方向上可發現相似之頻域能量分布,在圓管與圓棒加工對應之訊號分析方面,車削圓棒之訊號頻率分布較車削圓管時較不集中,且車削圓棒的訊號能量明顯大於圓管。以感測器到切削點的距離來看,距離越近,能量越大,反之亦然。在不同加工模式之分析上,在變換進給速度時,聲射訊號較不受影響,但聲音頻率訊號分布將受到進給變化之影響。最後比較單點加工與多點加工之分析,搪孔加工安裝在工件上Z軸方向的頻率分布比端銑削更集中於特定頻域範圍,且訊號能量小於端銑削。

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.
URI: http://hdl.handle.net/11455/2919
其他識別: U0005-2808201300312300
Appears in Collections:機械工程學系所

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