Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2809
標題: 切削路徑對應用主軸聲射與振動訊號之微銑削刀具磨耗偵測系統之影響分析
Analysis of Cutting Path Effect on Spindle AE and Vibration Based Tool Wear Monitoring System in Micro Milling
作者: 黃啟榮
Huang, Ci-Rong
關鍵字: 微細刀具監控;Micro tool condition monitoring;切削路徑影響;振動訊號;聲射訊號;Cutting path effect;Vibration signal;Acoustic Emission signal
出版社: 機械工程學系所
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摘要: 
隨航太、醫療與電子等產業的發展需求,零件的微型化及產品的高精度品質漸為現今加工技術發展之趨勢,因應此需求,微細切削加工之發展逐漸具其必要性,但微細切削加工過程中,微細刀具極易產生磨耗,而刀具磨耗對產品精度之影響性較傳統切削更為顯著,因此對於微細刀具狀態之監控扮演著重要角色。
本研究之目標為分析探討切削路徑變化於主軸振動與聲射(AE)訊號在微細加工刀具磨耗偵測之性能影響,分別分析使用振動訊號及聲射訊號於刀具狀態偵測時切削路徑變化對訊號之影響,並於不同的頻帶寬度與訊號選取長度探討切削路徑變化對系統辨識之影響度。辨識系統包含訊號轉換、特徵選取與辨識器設計三個主要模組,訊號經快速傅立葉轉換取得頻域訊號之能量分布後,透過群組分離準則選取刀具磨耗之特徵,並由費雪線性辨識函數判別刀具之狀態。在實驗訊號擷取方面,採用直徑700μm微型端銑刀對ISO TC120高碳鋼進行直線、直角轉向與圓弧路徑之槽銑加工,並透過架設於主軸上之三軸加速規與聲射感測器量取實驗訊號。
研究分析結果顯示,刀具磨耗與切削路徑變化影響主軸振動與聲射之訊號變動。主軸振動訊號於刀具進行直線路徑槽銑時,以其訊號建立之辨識系統具有辨識直線加工時刀具狀態之能力,但振動訊號因路徑變化而變動時,此變動對辨識系統造成干擾,使刀具狀態之辨識能力降低;透過增加訊號選取長度,能降低路徑變化之干擾,使系統於路徑變化下維持與直線加工相同之刀具磨耗狀態辨識能力。在主軸上量測之切削聲射訊號,雖然其受切削路徑變化之影響性較低,所建立之刀具狀態特徵選取較不受路徑變化所干擾;但是因每一把刀具之幾何外型均不同,聲射訊號易受切削時刀具幾何變動所影響,因此其所建立之系統辨識性能易受不同刀具變異所影響,造成刀具狀態辨識能力降低。利用訊號選取長度的增加與頻帶寬度的調整,同樣可以改善不同刀具幾何變異刀具磨耗狀態辨識之能力。

As the demand of the small feature and high accuracy for aerospace, biomedical, and electronic 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 cutting path effect on the performance of tool wear monitoring system integrated with the spindle vibration and acoustic emission (AE) signal obtained from the spindle housing, as well as the study of the effect of system parameters on the system performance.
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 length sizes of extracted signal, and the change of cutting path in micro milling were studied. In collecting the signal for system analysis and development, an experiment was implemented along with 700 μm diameter micro end mill and ISO TC-120 work-piece.
The results show that the AE and vibration signal collected on the fixture connected to spindle housing can be used to detect the change of tool wear on a micro end mill and the alteration of different cutting path in milling. The tool wear monitoring system was developed by the vibration signal from the straight line milling detect the tool condition of line cutting path well. As the cutting path switched, the varied signal influence the decrease of system classification rate. In the tool wear classification results, the effect of system parameters such as bandwidth size of frequency domain signal and the length sizes of extracted signal could reduce the effect of cutting path. In consideration of the AE signal case, the influence of cutting path is slight. The feature selection of system development would not affected by the effect of cutting path. But each micro end mill has the different geometry of tool flute, the AE signals are easy varied by the different cutting tool. The variability would influence the tool wear monitoring system ability. In the tool wear classification results, the effect of system parameters such as bandwidth size of frequency domain signal and the length sizes of extracted signal could reduce the influence of the different geometry of tool flute as well.
URI: http://hdl.handle.net/11455/2809
其他識別: U0005-2708201209533800
Appears in Collections:機械工程學系所

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