臺灣博碩士論文加值系統

銑削加工是一種非常常見的成型加工方法，而在銑削加工的過程中，不適當的加工參數會使刀具產生顫震，其顫震原因來自於刀刃與工件之間呈現週期性的不連續切削行為，造成切屑厚度週期性的變化，使得刀具產生自激性的震動以及不穩定的切削行為，而顫震會使銑削加工的不穩定性以及降低切削效率，造成尺寸精度、刀具壽命以及表面完整性的下降，因此本研究主要目標為建立完整之刀具壽命下，開發端銑刀的動態切削力模組，對於銑削加工潛在的顫震進行探討。以模組的方式模擬出之切削立，與實際切削所獲得之切削力進行時間及頻率域比較，我們發現時域、頻率域、短時距傅立葉變換、連率小波轉換以及希爾伯特-黃轉換進行顫震訊號分析，相較於使用傅立葉變換光譜法所獲得之結果是徹底地不同，傅立葉變換光譜法在應用於大量的非線性訊號效率極低，進行動態切削力模組模擬之訊號與實驗所獲得之切削力訊號比較，發現顫震頻率主要由兩個現象之頻率所構成，為刀刃通過工件時之頻率以及顫震造成的不穩定高頻，此外，以standard deviation以及本質模態函數方式所獲得之能量比，可有效率的辦別出刀具顫震，最後通過工件表面形貌、表面粗糙度和穩定性波瓣圖驗證其分析結果

The milling operation is the most common form of machining. Because the action of each cutting edge and workpiece is intermittent and periodical, the chip thickness varies periodically. This could lead to self-excited vibrations and unstable cutting which is called chatter vibration. Chatter causes machining instability and reduces productivity in the metal cutting process. It has negative effects on the surface finish, dimensional accuracy, tool life and machine life. Chatter identification is therefore necessary to control, prevent, or eliminate chatter and to identify the stable machining condition. A dynamic cutting force model of the end-milling process with tool runout error was established in this research to understand the underlying mechanism of chatter. The accuracy of the cutting force model in both time and frequency domains was evaluated by comparing to experimental force signals. Time-frequency analysis approaches, specifically short time Fourier transform, continuous wavelet transform and Hilbert-Huang transform, were utilized to give an utterly different perspective of chatter from the conventional Fourier spectrum which is insufficient in analyzing the signals of rich nonlinear characteristics. By comparing the simulation with experimental result, chatter frequency was found to consist of two major components, frequency modulation alongside tooth passing frequency caused by the increased tool runout error and the non-stationary high frequency from the regenerative vibration. Moreover, dimensionless chatter indicators, defined by the standard deviation and energy ratio of the specific intrinsic mode function, could identify the occurrence of chatter effectively. The analysis result was then validated by the workpiece surface topography, surface roughness and the stability lobe diagram

摘要 i
Abstract ii
Acknowledgement iii
Nomenclature iv
Contents v
Chapter 1 1
Introduction 1
1.1. Background 1
1.2. Objective and Scope 2
1.3. Outlines and Contribution of the Chapters 3
Chapter 2 4
Literature Review 4
2.1. Chatter Vibrations in Milling 4
2.2. Signal Analysis Approaches 6
Chapter 3 10
Dynamic Cutting Force Model 10
3.1. Regenerative Chatter Model 10
3.2. Dynamic Cutting Force Model 15
Chapter 4 17
Experimental Setup and Model Verification 17
4.1. Overview and Aim 17
4.2. Experimental Setup 17
4.2.1. Machine, Cutter and Workpiece 17
4.2.2. Cutting Force Measurement Equipment 19
4.2.3. Surface Topography Measurement Equipment 20
4.2.4. Surface Roughness Measurement Equipment 20
4.3. Cutting Force Coefficients 21
4. 4. Experimental Design Parameters 25
4.5. Tool Tip Dynamics 27
4.5.1. Impact Testing 27
4.5.2. Modal Analysis 27
4.5.3. Stability Lobe Diagram 31
4.6. Simulation and Experimental Results 33
Chapter 5 36
Chatter Detection Methodology 36
5.1 Short-Time Fourier Transform Analysis 36
5.2. Continuous Wavelet Transform Analysis 38
5.3. Time-Frequency Analysis Based on HHT 41
5.3.1. Chatter Detection Methodology 41
5.3.2. Results and Discussions 44
5.3.3. Dimensionless Indexes for Chatter Identification 50
5.3.4. Method Verification 51
5.4. Chatter Identification of Small Size End Mill 54
5.4.1. Overview and Aim 54
5.4.2 Experimental Setup 54
5.4.3. Chatter Identification by Time-Frequency Analysis 57
Chapter 6 60
Conclusions and Future Works 60
6.1. Conclusions 60
6.2. Future Works 60
Bibliography 61

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