研究生: |
蔡承澤 CHENG-TSE TSAI |
---|---|
論文名稱: |
基於時頻分析之超音波刀具可靠度評估 Evaluation of the Ultrasonic Cutting Tools Reliability by Time-Frequency Analysis |
指導教授: |
劉孟昆
Meng-Kun Liu |
口試委員: |
周振嘉
Chen-Chia Chou 藍振洋 LAN,CHEN-YANG |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 102 |
中文關鍵詞: | 超音波切割刀 、時頻分析法 、訊號處理 、希爾伯特-黃轉換 、支持向量機 |
外文關鍵詞: | Ultrasonic machining, Time-Frequency Analysis, Honeycomb- structure, Signal processing, Hilbert-Huang Transform |
相關次數: | 點閱:396 下載:0 |
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「超音波刀具(Ultrasonic Cutting Tools)」全名為「蜂巢狀複合材料超音波輔助切割刀具」,是為航太領域中專門切割蜂巢結構(Honeycomb Structure)所研發之工具。此種刀具主要由碳化鎢所組成且價格昂貴,其加工方式為將刀具藉由螺紋鎖緊於超音波變幅桿末端,利用高頻超音波振動切割紙蜂巢結構。一般將超音波主軸安裝在CNC工具機上以加工複雜曲面。然而超音波刀具的可靠度參差不齊,造成換刀時機不易預測,常有刀具尚可切削卻遭淘汰,造成經濟效益低落。
本研究透過高靈敏度麥克風,擷取超音波刀具在無負載時所發出的音壓訊號,藉希爾伯特-黃轉換(Hilbert-Huang Transform)找出刀具之瞬時頻率,進而診斷超音波刀具潛在之可靠度。其中音壓擷取實驗使用自行設計之變幅桿(horn)作為量測的基準,藉由ANSYS的模態分析(Modal Analysis)與簡諧響應分析(Harmonic Response Analysis)做為其前導設計。利用希爾伯特邊際頻譜可擷取特徵找出其特徵參數,並使用支持向量機(Support Vector Machine, SVM)之機器監督式學習技術做為分類器,可判斷刀具的可靠度,除了可以當作交貨時的驗刀機制之外,並可預測刀具的使用期限,達到降低成本之目的。
Ultrasonic cutting tool is specifically used in the manufacturing of honeycomb structure in aerospace engineering. This tool made of expensive tungsten carbine is locked at the end of ultrasonic horn by screw thread, and it generates high frequency ultrasonic vibrations to cut the honeycomb structure. In general, the ultrasonic spindle is mounted on the CNC machine to work on complex surface structure. However, the reliability of the ultrasonic tool is unsteady and its life cycle is unpredictable. It renders low economic efficiency when an eligible tool is replaced in advance to prevent premature failure.
In this research, a high sensitive microphone is used to capture the sound pressure generated by the ultrasonic cutting tool without loading. The time-frequency spectrum generated by Hilbert-Huang transform (HHT) is applied to evaluate the potential reliability of the tool. To conduct the experiment, a customized horn is designed by using modal analysis and harmonic response analysis in ANSYS. The vibration features can be captured by marginal spectrum, and it uses support vector machine (SVM), a supervised learning algorithm, as the classifier to identify the reliability of the tool. The proposed mechanism not only predicts the reliably of the cutting tool, but also can be used as an evaluation standard for the new purchase. Hence the manufacturing cost can be reduced.
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