Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/2856
標題: 應用主軸振動與聲射訊號於鑽頭狀態偵測之研究
Applications of Spindle Vibration and AE Signal for Tool Condition Monitoring in Drilling
作者: 許育瑋
Hsu, Yu-Wei
關鍵字: 鑽頭刀具監控;Drill tool condition monitoring;振動訊號;聲射訊號;Vibration signal;Acoustic Emission signal
出版社: 機械工程學系所
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摘要: 
近年來隨著科技不斷地進步,提升競爭力是現今產業發展非常重要的課題。在機械加工中,提升加工效率與品質以及如何降低成本便是提升競爭力重要的方式,因此生產線的自動化、需求陸續增加。在加工過程中,刀具會有磨耗及斷裂狀況發生,造成加工機具產生振動、加工工件表面粗糙度和尺寸精度變差,嚴重時更會造成加工機故障等,進而使生產線停擺。因此刀具線上偵測系統在製造自動化之發展中,扮演重要的角色。
本研究探討主軸振動與聲射訊號於切削加工中鑽頭狀態偵測之應用性能,利用裝置在主軸上之加速規與聲射感測器分別擷取加工中振動與聲射訊號之變化,經由FFT轉換或小波轉換取得頻域訊號特徵,接續藉由群組分離法則挑選刀具磨耗或刀具斷裂相關之特徵訊號後,再以費雪線性辨識函數判別刀具加工中之狀態。實驗中刀具採用直徑2mm與3mm鑽頭,工件材料為鋁材6061,加工時刀具磨耗與斷裂狀態對應之振動與聲射訊號,則以Labview系統擷取。在刀具磨耗相關訊號分析部分,分別探討不同切削深度位置與不同切削參數設定下對於訊號之影響、不同訊號頻帶寬度對辨識系統之影響。而斷裂訊號分析部分,探討使用單一參數所建立之辨識模型,對於不同切削參數辨識能力分析。
訊號分析結果顯示,振動與聲射感測器裝置於主軸上外掛夾具上,量測之訊號可偵測到鑽頭刀具磨耗變動狀態。在鑽孔切削過程中,觀察三種切削參數設定下不同切削深度位置對應之振動與聲射訊號,存在著因切屑排除問題造成系統變異產生之特徵訊號變化。在刀具狀態判別方面,不同切削參數加工所獲得之訊號,將影響刀具磨耗狀態之準確性;使用單一切削參數建立的狀態辨識模組,將造成不同切削參數加工時刀具磨耗辨識上的誤判。如採用不同切削參數所獲得之訊號以建立狀態辨識模組,可提高對不同切削參數下刀具磨耗之識模率。刀具斷裂狀態判別方面;使用單一切削參數加工之振動訊號所建立的狀態辨識模組,對不同切削參數刀具斷裂之偵測,成功率可達100%。

In metal cutting process, the tool wear and tool breakage will lead to decrease of machining efficiency due to the increase of machine vibration, the increase of surface roughness on finished surface, the decrease of geometry accuracy and the damage of the machine tool. Therefore, the development of the tool condition monitoring system plays an important role in developing the manufacturing automation technology.
This research focus on the study of applying spindle vibration and acoustic emission (AE) signals on the tool condition monitoring in drilling. In the signal processing, the time domain signal was first transformed based on FFT and Wavelet transform to create features, followed by the feature selection based on the class mean scatter criteria to extract the feature closely related to the tool condition. Finally, the Fisher linear classifier was developed to classify the tool condition based on the selected features. To collect the data to develop and test the developed monitoring system, experiments along with two kinds of tools with diameter of 2mm and 3mm were conducted to collect the spindle vibration and AE signals in drilling 6061 Aluminum with various tool condition. Two Data acquisition boards integrated with LabView software were used for data collection. In tool wear monitoring, the effect of tool size, data collection point, and cutting parameters on the signals, as well as the effect of bandwidth size selection on the performance of tool wear monitoring system were analyzed. In tool breakage monitoring, the performance of developed system was investigated by applying to the cases with different cutting parameters from the case for system development.
The results show that the spindle vibration and AE signal collected on the fixture connected to spindle housing can be used to detect tool wear and tool breakage in drilling. Moreover, the signals collected from drilling processes with various cutting parameters all demonstrate that the continuous change of feature characteristics as drilling proceed can be observed. This phenomenon might be caused by the random occurring of entangled chip or various chip/drill relationship when chip coming out of drilled hole. In the analysis of system performance in tool wear monitoring, the change of cutting parameters will change the frequency features and lead to the low classification rate when the system developed with cutting parameters differing from those for the test case. However, the model developed based on the mixed signals collected from all case with various cutting parameters will improve the classification rate dramatically. In the analysis of system performance for tool breakage monitoring, 100% classification rate can be obtained, even by developing the system based on the signals collected with cutting parameters differing from the test case.
URI: http://hdl.handle.net/11455/2856
其他識別: U0005-2608201220481300
Appears in Collections:機械工程學系所

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