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研究生: 吳怡霏
Ulfa Fairuz Izdihar
論文名稱: 基於工件顯微檢測及反應曲面建模的車削參數優化
Optimization of Turning Process Parameters Based on Microscopic Workpiece Inspection and Response Surface Modeling
指導教授: 林柏廷
Po-Ting Lin
口試委員: 鍾俊輝
Chunhui Chung
姚賀騰
Her-Terng Yau
陳明志
Ming-Jyh Chern
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: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 127
中文關鍵詞: turning parametersmicroscopic inspectionoptimizationResponse Surface Methodology
外文關鍵詞: turning parameters, microscopic inspection, optimization, Response Surface Methodology
相關次數: 點閱:278下載:5
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In the turning process, the workpieces' results, including the surface finish and dimensions, are needed to satisfy the specifications of the products. Simultaneously, to achieve the results, the vibration and other forms of energy dissipations will be occurred and affected the process. Vibration appears under the excitation applied by the material deformation process during the turning process. For the results as products, surface roughness is more challenging to achieve if the vibrations occur excessively. This study concerned the optimization of cutting parameters, including spindle speed, feed rate, and depth of cut (DOC) based on an experimental by using L9 Taguchi, microscopic workpiece inspection, and Response Surface Methodology (RSM). The study aims to achieve minimum vibration and improve the quality of surface roughness of SNCM439 materials. This study showed that the depth of cut is the parameter that affects the surface roughness the most, and spindle speed is the parameter that affects the vibration the most. As a result, the optimal combination of cutting conditions used is 800rpm for spindle speed, 0.15mm/rev for feed rate, and 1.75mm for the depth of cut to optimize the vibration and surface roughness. Another way to evaluate the cutting performance is to estimate the total cost of machining. Our study showed that the cutting parameters for minimal vibration or optimal surface roughness might not be the designs with minimal costs.

Abstract Table of Contents Nomenclature List of Tables List of Figures 1. Introduction 1.1. Introduction to Optimization in Turning Process 1.2. Research Development of Optimization in Turning Process 1.2.1. Literature Reviews 1.2.2. Summary 1.3. Scope of The Present Study 2. Theoretical Background 2.1. Metal Machining 2.1.1. Turning Process 2.1.2. Cutting Conditions 2.1.3. Cutting Tool 2.2. Metal Materials for Turning Process 2.3. Surface Roughness in Turning 2.4. Vibration in Machining Process 2.5. Design of Experiments by Taguchi 2.5.1. Steps of Design of Experiments 2.5.2. Design of Experiments using Orthogonal Arrays 2.6. Response Surface Methodology 2.7. Machining Cost 3. Experimental Methodology 3.1. Flowchart of Research 3.2. Experimental Procedure 3.2.1. Machine, Material, and Tools 3.2.2. Machining Setup 3.2.3. Measurement Setup 3.3. Design of Experiments (DOE) 3.3.1. Selecting Response, Factors, and Levels 3.3.2. Orthogonal Matrix 3.4. Response Surface Methodology (RSM) 3.4.1. Defining Problem and Objectives 3.4.2. Steps of Response Surface Methodology in Minitab 4. Results and Discussions 4.1. Data Analysis for Surface Roughness 4.1.1. Results Data 4.1.2. ANOVA Table 4.2. Data Analysis for Vibration 4.2.1. Results Data 4.2.2. ANOVA Table 4.3. Tool Wear 4.4. Response Surface Methodology using Full Quadratic Regression 4.4.1. RSM for Surface Roughness 4.4.2. RSM for Vibration 4.4.3. Response Optimizer 4.5. Machining Cost Comparison 5. Conclusions References Appendix A: G-code Appendix B: 3D Surface Roughness Curriculum Vitae

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