臺灣博碩士論文加值系統

本研究旨在應用特殊電腦數值控制氣囊拋光機，建立光學玻璃表面品質拋光策略，研究中透由氧化鈰聚胺脂拋光墊(LP66)於石英光學玻璃表面進行拋光研究，期可達到穩定控制材料移除率目的，同時找出提升光學玻璃表面品質的拋光策略。研究發現，高精度光學玻璃於拋光過程中，特定的刀具路徑常會伴隨中頻空間頻率誤差，使鏡片表面產生定頻紋理或不規則波紋，弱化了成像品質。為此，本論文為了降低中頻空間頻率誤差，首先將拋光各種參數組進行最佳化，其拋光參數包含工具下壓量、主軸轉速、路徑間距與表面進給速率等。接著基於拋光基本公式-普雷斯頓方程式與赫茲接觸理論，將拋光主軸與光學玻璃表面接觸的拋光行為視為彈性固體的接觸，建立刀具影響函數理論模型。
本研究進一步嘗試探討與預測出穩定材料移除深度的理想拋光參數組合。研究中，經由所建立模型，將數值分析模擬結果，透過實際拋光進行數值比較，並據以修正其數學模型。研究結果發現，氣囊拋光刀具下壓量，僅會對於材料移除量有所影響，對於表面紋理的品質並不會造成太大的改變。研究結果發現，主軸轉速與進給率的配置為主要的影響因子，使其在拋光過程中穩定進行拋光作業，實現高精度的光學表面紋理品質。
本研究發現改變路徑間距參數，可有效改善定頻紋理與抑制不規則中頻空頻空間頻率誤差，其光學玻璃表面紋理可實現均方根粗度值達1.6 nm。透由所建立的刀具影響函數理論模型之模擬結果與實驗結果比較，再次證實路徑間距的設定確實對於光學玻璃表面紋理品質造成影響，其同位置所受到的重疊數值，運算結果為4.82。接著將拋光路徑間距所產生的疊加效應影響納入理論模型，再加入形貌誤差修正實驗驗證，其模擬與實驗的最大深度差值為123.6 nm，兩者結果相似約85%，亦即證明理論模型可用於模擬拋光製程的最佳參數條件，研究成果將可實現穩定控制材料移除量之目的，此項技術將可應用於光學玻璃元件拋光製程技術。

This study presents an optical polishing quality strategy using automated computer numerical control (CNC) bonnet polishing to produce a high-quality level surface at a stable material removal rate (MRR). Owing to the specified toolpaths integral to CNC bonnet polishing, the resulting polished surfaces generally have mid-spatial-frequency (MSF) errors, which can occur as periodic surface ripples or irregular waviness. These surface textures could degrade image quality in many devices or cause defocusing and energy loss in high-energy laser systems. In this study, the MSF surface textures of fused silica are investigated by developing a CNC bonnet polishing technique using a cerium-oxide-filled polyurethane pad (LP66) that employs a cellular polyurethane material designed to handle the high flatness and surface finishing requirements of optical glass materials. Various combinations of tool offset, head speed, track spacing and surface feed rate settings are studied to determine the optimal polishing parameters for minimizing MSF errors. To further explore and predict the ideal polishing parameters for a stable material removal rate, a tool influence function (TIF) mathematical model is built based on the Preston equation of material removal rate for polishing, with the head spindle contact and polishing behavior of optical glass surfaces treated as contact between elastic solids. Then, polishing is simulated using the mathematical model, the material removal characteristics of the simulated and experimental results are compared and the mathematical model is modified accordingly. The experimental results demonstrate that the head speed and surface feed rate significantly affect the surface texture during bonnet polishing. Although the tool offset does not cause surface texturing, it does affect the material removal rate. A series of optimization experiments is conducted, ultimately leading to the effective removal of irregular surface ripples and a reduction in MSF errors. By optimizing the polishing parameters, a high surface quality with extremely accurate optical performance is achieved, along with a root-mean-square error of 1.6 nm. The effect of TIF superposition is considered in the polishing strategy because the track spacing distance experiment results showed that the superposition of the polishing path was responsible for differences between the simulated and experimental results. First, the superposition value is calculated as 4.82 for the same point by superposition of the modeled TIF. Then, the TIF model is adjusted to include the impact of superposition, and the output of the TIF model is recalculated to further refine the polishing strategy. Finally, the material removal function is modified to attain an MRR difference value of 0.12 m. When the results are compared, the simulated material removal depth accounts for up to 85% of the experimental material removal depth. These experimental and simulated results indicate that the TIF model can provide preliminary predictions of the effects of various combinations of polishing parameters on the material removal rate and demonstrate the potential applications of LP66 in CNC bonnet polishing for optical glass component processing technologies.

中文摘要 II
ABSTRACT III
ACKNOWLEDGMENTS V
CONTENTS VI
LIST OF TABLES VIII
LIST OF FIGURES X
CHAPTER 1 INTRODUCTION 1
1.1 Backgrounds of the Research 1
1.2 Motivations 2
1.3 Objectives 4
1.4 Research Methodology 4
1.5 Thesis Framework 6
CHAPTER 2 LITERATURE REVIEW 7
2.1 Precession Bonnet Polishing Technology 7
2.2 Characteristics of Material Removal 14
2.3 Surface Texture Analysis 20
2.4 Concluding Remark 24
CHAPTER 3 EXPERIMENTAL OBSERVATIONS 25
3.1 Experimental Setup 25
3.2 CNC Bonnet Polishing Machine (Zeeko IRP 1000) 26
3.3 Aspheric Stitching Interferometer (QED-ASI) 27
3.4 Interface Materials 28
3.5 Experimental Design 30
CHAPTER 4 STUDY OF THE SURFACE QUALITY 31
4.1 Texture Quality Effect for Tool Offset 32
4.2 Texture Quality Effect for Head Speed 34
4.3 Texture Quality Effect for Track Spacing 37
4.4 Texture Quality Effect for Surface Feed Rate 41
4.5 Experiment for Equal Amounts of Material Removal 45
4.6 Experimental Validation in Optimized the Combination 49
4.7 Concluding Remark 52
CHAPTER 5 MODELING TOOL INFLUENCE FUNCTION 53
5.1 Building the Tool Influence Function Model 54
5.2 Material Removal Depth Distribution Effects for a Single Pass 58
5.3 Material Removal Depth Distribution Effect from Repetitive Paths 60
5.4 Tool Influence function Model Effect for Tool Compensation 64
5.5 Modified Tool Influence Function Model 69
5.6 Experimental Validation Using a Tool Influence Function 73
5.7 Concluding Remark 82
CHAPTER 6 CONCLUSIONS AND FUTURE WORK 83
6.1 Conclusions 83
6.2 Future Work 86
REFERENCES 87

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