臺灣博碩士論文加值系統

　　現今於模具製造產業為了提高製品的品質及性能，常以硬銑削來取代精磨製程，此方法可降低產品週期時間、提高生產率及最終製品品質亦可獲得顯著提升。
　　本研究主要基於田口方法與反應曲面法針對SKD61模具鋼之硬銑削之製程參數對切削力、表面粗度及材料移除率之影響。本研究以田口方法進行實驗設計並以銑床結構之動態分析為加工參數選擇之依據。在量測部分，以動力計來量測切削力而中心線平均粗糙度則藉由三豐SJ-400表面粗度儀所獲得，最後以變異數分析來獲得各切削參數之反應特性。在硬銑削製程中的預測模型以二次數學模型來預測切削力、表面粗度及材料移除率。
　　本研究以低切削力、低表面粗度及高材料移除率為期望函數進行目標優化。結果顯示表面粗度及材料移除率之量測值與預測值兩者間的誤差百分比為9.84%與2.27%。此外各切削分力之量測值與預測值的誤差百分比為8.78、7.46及9.53%。
　　最終經優化後之銑削參數其表面粗度為0.193μm，由此可知在模具製造工業中硬銑削可用來取代精磨製程。反應曲面法可有效應用於JIS SKD61模具鋼硬銑削製程的參數優化。

　　Nowadays, in order to enhance performance and product quality in the mold and die manufacturing industry, the finishing grinding process is often replaced by a hard milling operation; this way, the product cycle time can be decreased, productivity increased, and the quality of finished products can be significantly improved.
　　In this study, an attempt is made to analyze the effect of process parameters on cutting force components, surface roughness and material removal rate (MRR) in the hard milling of SKD 61 steel based on a combination of the Taguchi method and Response surface methodology (RSM). The machining process parameters are selected based on the structural dynamic analysis of the milling machine tool. A set of experiments is designed according to the Taguchi technique. The cutting force values are measured by a Dynamometer. The average surface roughness is measured by a Mitutoyo Surftest SJ-400. And then, analysis of variance (ANOVA) is performed to determine the influences of the cutting process parameters on the given response characteristics. Quadratic mathematical modeling is introduced for predicting the cutting forces, surface roughness and MRR during the hard milling process. Predicted values obtained from the developed model and experimental results are compared, and it shows that the predicted values are in reasonable consensus with the observation of experiments.
　　In an effort to obtain the small cutting forces and surface roughness and large MRR, a simultaneous objective optimization is employed based on the desirability function. The results show that the percentage error between measured and predicted values of surface roughness and MRR are 9.84 % and 2.27 %, respectively. In addition, the percentage error between measured and predicted values of the cutting force components (Fx, Fy, and Fz) are found to be (8.78, 7.46 and 9.53) %, respectively, which is found to be small.
　　Eventually, the milled surface roughness under the optimized machining parameters is 0.193 µm, which can be justified so that the finish hard milling is able to replace the finish grinding in the mold and die manufacturing industry. Therefore, the RSM could be effectively applied to optimize simultaneously some response characteristics during the hard milling process of JIS SKD 61 alloy steel.

中文摘要 i
ABSTRACT ii
Acknowledgments iv
Contents v
List of Figures viii
List of Tables xii
List of Symbols and Abbreviations xiv
Chapter 1. Introduction 1
1.1. Motivation 1
1.2. Scopes 5
1.3. Contributions 5
1.4. Limitation 6
1.5. Overview of the dissertation 6
Chapter 2. Overview of hard machining process 9
2.1. Concepts of hard machining operation 9
2.2. Advantages and disadvantages of hard machining 9
2.3. Several operations of hard machining 10
2.3.1. Hard turning 10
2.3.2 Hard milling 11
2.4. Some of the response characteristics in hard machining 13
2.4.1. Surface roughness 13
2.4.2. Material removal rate in milling 15
2.4.3. Tool wear in milling 15
Chapter 3. Dynamics of hard milling process 17
3.1. Modelling of cutting forces in helical end milling 17
3.2. Determination of average cutting force coefficients for milling 20
3.3. Chatter vibration 22
3.4. Stability analysis of end milling 24
3.4.1 Dynamic model of milling 24
3.4.2. Chatter stability analysis 26
Chapter 4. Research methods 30
4.1. Taguchi method 30
4.2. Response surface methodology 32
4.3. Optimization based on desirability function 35
Chapter 5. Determination of machining conditions for hard milling 39
5.1. Calculation of average cutting force coefficients 40
5.1.1. Determination of input parameters for experiments 40
5.1.2. Experimental set-up and milling process 41
5.1.3. Measurement of cutting force components 43
5.1.4. Calculation of average cutting force coefficients 44
5.2. Measurement of frequency response functions 45
5.3. Determination of stability lobes by using CutPro software 53
Chapter 6. Optimization of response characteristics in hard milling 56
6.1. Optimization of cutting forces and MRR 56
6.1.1.Results 56
6.1.2.Discussion 59
6.2. Optimization of surface roughness and MRR 72
6.2.1.Results 72
6.2.2. Discussion 74
6.3. Simultaneous objective optimization for the cutting forces, surface roughness and MRR 82
6.4. Comparison of cutting forces between the experimental and simulated results 88
6.5. The use of the proposed models 91
Chapter 7. Conclusions and future works 95
7.1. Conclusions 95
7.1.1. Conclusions for optimization of cutting forces and MRR 95
7.1.2. Conclusions for optimization of surface roughness and MRR 96
7.1.3. Conclusions for simultaneous optimization of cutting forces, surface roughness and MRR 97
7.2. Suggestions for future work 98
List of publications 100
References 101
Appendix 110

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