臺灣博碩士論文加值系統

導光板為使背光模組提供液晶顯示器充分光源的關鍵組件。而射出成型是生產 三維複雜形狀成品的最佳方式之一，因此也是製造導光板的的重要關鍵技術。然而射出成型加工參數的設定一直存在造成導光板收縮變形而影響品質好壞的問題。冷卻時間、模具溫度、融膠溫度、射出速度、射出壓力、保壓壓力、保壓切換點、保壓時間等射出成型加工參數的改變對於導光板的 V-cut 微結構尺寸有直接的影響。所以，本研究主要探討精密射出成型加工參數與LCD導光板的品質特性之間的關係。應用田口方法、灰色關聯分析與倒傳遞類神經網路來尋找導光板的最佳加工條件與建構導光板於精密射出成型之品質預測系統。
首先，決定導光板的品質特性，其為V-cut微結構的深度與角度以及導光板的輝度均勻度，然後利用田口方法規劃加工參數之水準值，再依照直交表進行實驗。但是，由於田口方法大多使用於單一品質特性之最佳化，然而決定單一品質特性之最佳化加工參數，常常是無法完全表示整體品質的加工參數最佳化。因此本文利用灰色關聯分析來整合多重品質特性，以決定導光板的最佳加工條件。並且進行變異數分析，以對實驗結果進行探討。從變異數分析可以得到對導光板品質特性影響較大的顯著因子，亦即控制這些因子，便可以有效控制導光板的品質特性。經由確認實驗以95%信心區間驗證其實驗具有可靠性與再現性。
本文所建構的預測系統為倒傳遞類神經網路，其為具有輸入層、隱藏層與輸出層的類神經網路。射出成型的八個加工參數做為倒傳遞類神經網路的輸入變數，而導光板V-cut的深度與角度以及導光板的輝度均勻度作為輸出變數，以建立倒傳遞類神經網路架構。再以田口方法來規劃倒傳遞類神經網路的學習參數，可以改善傳統試誤法的缺點，使網路更有效率的快速收斂，進而快速的找出較佳的學習參數組合。實驗結果顯示，本文所建構的預測系統可以準確的預測導光板的品質特性。

A light guide plate is a key component that enables backlight modules to provide sufficient light source for LCDs (Liquid Crystal Displays). Injection molding is one of the best methods for producing a complex-shaped three dimensional product, and is therefore a crucially important core technology for the production of light guide plates. However, the specification and setting of the injection molding processing parameters has always possessed the problem of resulting in the deformation of the light guide plate because of shrinkage and contraction, causing variations in the quality of the light guide plates produced. Hence, this study aims to investigate the relationship between precision injection molding process parameters and light guide plate properties using Taguchi method, grey relational analysis, and back-propagation neural network to determine the optimal processing parameters for light guide plates and to establish a quality prediction system for injection molding of light guide plates.
Firstly, the quality characteristics of this experiment are the depth and angle of the V-cut microstructure as well as luminance uniformity. It then designs the level of processing parameters using the Taguchi method and moves on to conduct orthogonal array experiments. Nonetheless, since the Taguchi method is mostly used to optimize single quality characteristic, which often fail to represent processing parameters optimization of the overall quality. This study takes advantage of grey relational analysis and integrates multiple quality characteristics to determine the optimal processing parameters for light guide plates. Analysis of variance (ANOVA) will be performed to look into the results obtained from the experiment. From ANOVA the significant factors can be obtained which have the greatest effect on the light guide plate quality characteristics, in other words, by controlling these factors, the quality characteristics of the LCD light guide plate can be effectively controlled. Then, the reliability and reproducibility of the experiment were verified by confirming a confidence interval (CI) of 95%.
Additionally, the back-propagation neural network adopted in this study is a neural network that contains input layers, hidden layers, and output layers with a structure that is established by using eight injection molding process parameters as input variables and the depth, angle and luminance uniformity of light guide plates V-cuts as output variables. Here, the Taguchi method is employed to design the learning parameters for the back-propagation neural network, which is free of the deficiencies of the traditional trial-and-error method and speeds up network convergence to determine preferable combinations of learning parameters. As the experiments reveal, the prediction system established in this study is effective in accurate prediction of the qualities of light guide plates.

Abstract II
Acknowledgement IV
Contents V
List of Figures VII
List of Tables IX
Chapter 1. Introduction 1
1.1. Research Motivations 1
1.2. Research Objectives 2
1.3. Literature Survey 8
1.4. Overview of This Dissertation 15
Chapter 2. Concept and Principle of Injection Molding 17
2.1. The Injection Molding Process 18
2.2. Injection Molding Parameters 20
2.3. Mold Filling 23
2.4. Assumptions at Start of Filling 25
2.5. Process Physics 26
2.6. Packing/Hold Stage 27
2.7. Cooling 28
2.8. Plastification and Melt Temperature Control 29
2.9. Ejection 30
Chapter 3. Experimental Approaches 32
3.1. Taguchi method 32
3.1.1. Orthogonal array 32
3.1.2. Parameter design 33
3.2. Grey Relational Analysis 34
3.3. Analysis of Variance 37
3.4. Confirmation Experiment 40
3.4.1. The CI of the theoretically predicted value 41
3.4.2. The CI of the calculated experiment value 41
3.5. Back-Propagation Neural Network 42
Chapter 4. Results and Discussion 46
4.1. Experimental Design 46
4.1.1. Materials and equipments 47
4.2. Taguchi Experimental Method 54
4.2.1. Select orthogonal array 54
4.2.2. Plan and execute tests 55
4.3. ANOVA Statistical Results 63
4.4. Application of Grey Relational Analysis 68
4.5. Confirmation Experiments 71
4.6. Establishment of Quality Prediction System 75
Chapter 5. Conclusions 81
References 84
Vita 90

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