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Study of Vibration and AE Signals for Tool Wear Monitoring in the Micro Milling
|關鍵字:||Micro tool condition monitoring;微細刀具監控;Decision fusion;Vibration signal;Acoustic Emission signal;判別融合;振動訊號;聲射訊號||出版社:||機械工程學系所||引用:|| Kunpeng, Z. , San, W. Y. and Soon,H.G., 2009, ”Wavelet analysis of sensor signals for tool condition monitoring:A review and someresults,” International Journal of Machine Tools & Manufacture Vol.49, pp.537-553.  Rehorn,G,Jiang,J., and Orban,E. ,2005, ” Sate-of-the-art methods and results in tool condition monitoring: a review,” Int J Adv Manuf Technol Vol.26, pp. 693–710.  Jantunen,E., 2002, ” A summary of methods applied to tool condition monitoring in drilling ” , International Journal of Machine Tools & Manufacture Vol.42 , pp.997–1010.  Li,X. 2002, ” Review A brief review: acoustic emission method for tool wear monitoring during turning ” , International Journal of Machine Tools & Manufacture Vol.42, pp.157–165.  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本研究之目標為分析探討主軸振動與聲射(AE)訊號在微細加工刀具磨耗偵測之應用特性，分別使用振動訊號及聲射訊號分析訊號特徵以及系統各參數對於刀具狀態之判別影響，以及利用判別融合(Decision fusion)整合兩感測器之刀具判別狀態情形，提升刀具狀態之判別之穩定度。研究之辨識系統包含了訊號轉換、特徵選取與辨識器設計三個主要模組，訊號經FFT 轉換，經由群組分離準則(Class mean scatter criteria)選取特徵訊號後以費雪線性辨識函式(Fisher linear discriminant function)判別加工中刀具之狀態。在訊號特徵與參數影響分析方面，分別探討三軸加速度與聲射(AE) 訊號之訊號特徵以及各訊號頻帶寬度、特徵選取個數以及加工過程刀具與筒夾間邊界條件對辨識系統之影響。
訊號分析結果顯示，AE 與 振動感應器裝置於主軸上之外掛夾具上，量測之訊號可偵測到微銑刀刀具磨耗之變動狀態。三軸振動訊號能量隨著刀具磨耗量之增加穩定的成長，實驗過程如改變筒夾夾持刀具之狀態則造成刀具磨耗之振動訊號能量不隨刀具磨耗量增加而增加，且呈無規則變化。在聲射訊號方面，主軸上量取刀具磨耗之聲射訊號，在頻率60KHz至70KHz訊號能量將較刀具未摩耗時增加，在頻率330KHz至400KHz間之頻率訊號能量則會因刀具與筒夾間狀態變動產生大的差異變化。在刀具狀態判別方面，三軸振動訊號考慮不同軸向訊號，特徵值個數與頻帶寬度對辨識率之影響並不相同，採用X軸振動訊號以及選擇235Hz頻寬特徵時，選取兩個特徵值輸入辨識器可達到90%刀具辨識率， Y與Z軸振動訊號則於選擇頻帶寬度120Hz時，分別輸入兩個與三個特徵個數，對於刀具辨識率可達到85%。對於調整過特徵向量之費雪線性類器，增加特徵個數至三與四個，其刀具辨識率可提升5%至10%。在AE 訊號方面，選擇頻帶寬度有助於刀具狀態辨識能力提升，頻帶於53.3KHz輸入兩個特徵值其平均刀具辨識率可達到75%，以判別融合之方式整合三軸振動訊號與聲射訊號可使刀具狀態辨識平均成功率可達到95%。
As the demand of the small feature and high accuracy for optical, electronic, and biomedical devices continuously increases, the micro mechanical machining plays an important role for improving their manufacturing quality and efficiency. Due to the higher tool wear rate than conventional counterpart, the tool wear monitoring in the micro machining draws much more attention than before.
The objective of this thesis is to analyze the performance of tool wear monitoring system integrated with the spindle vibration and acoustic emission signal obtained from the spindle housing, as well as the study of the effect of system parameters on the system performance. For improving the classification rate, a decision fusion algorithm was also adopted to integrate the decision made from the spindle vibration and AE signal for tool wear monitoring.
A micro tool condition monitoring system integrated by sensor system, signal transformation, feature selection, and classifier was developed in this study. In which, the FFT transformation was used for transforming the time domain signal to the frequency domains, the class mean scatter criteria was used to select the features closely related to the tool wear condition, and the Fisher linear discriminant function was the basis for designing the classifier. In the analysis of the parameters effect on the system performance, the bandwidth sizes of frequency domain signal, the number of the selected features and the change of contact between the tool and tool holder were studied.
The results show that the AE and vibration signal obtained from the spindle housing can be used to detect the change of tool wear on a micro end mill. The energy of the vibration signal was observed to increase as the tool wear proceeds. However, when the tool was reinstalled between each pass of cutting, the same trend will not be kept. For the AE signal, the energy of signal between 60 kHz to 70 kHz was observed to increase as tool wear proceeds. In addition, the energy of signal between 330 kHz to 400 kHz was observed to change as the tool was reinstalled between each pass of cutting. In the tool wear classification results, the effect of system parameters such as bandwidth size of frequency domain signal, and the number of features selected on the system varied for the different direction of vibration signal. The best parameters selected for the case with the X direction vibration signal was two features selection along with bandwidth size of 235Hz, but two or three features selection along with bandwidth size of 120Hz was found to be best for the Y and Z direction vibration signal. Moreover, by modifying the Fisher linear discriminant function, the increase of feature number can improve the classification rate by 5% to 10%. In consideration of the AE signal case, the increase of the bandwidth size was observed to improve the classification rate to 75% with bandwidth size of 53.3 kHz. Finally, by integrating the decision from the discussed four signals, the classification can be improved and reaches 95%.
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