臺灣博碩士論文加值系統

注塑成型 (IM) 是一種複雜的製造過程，其特點是具有非線性行為。與簡單回歸等經典線性建模技術不同，許多機器學習模型能夠適應輸入和輸出參數之間的非線性行為和相互作用。人工神經網路 (ANN) 尤其在涉及非線性建模的問題上表現出色。在注塑成型中實施 ANN 進行預測建模，由於所需的訓練數據量相當大，遇到了顯著的障礙。遷移學習 (TL) 通過利用一個數據集的知識來提高另一個數據集上模型的性能，提供了一個可行的解決方案。本研究的目的是利用遷移學習，通過使用 ANN 預測注塑成型中的能耗和產品重量。要獲取能耗數據，需要直接進行實驗。因此，本研究將採用完全因子設計 (DoE) 獲取一個既穩健又適合於訓練、驗證和測試的數據集。在估算不同材料的重量和能耗方面，研究表明使用遷移學習方法能大大加快學習過程。經驗證據表明，採用遷移學習技術顯著提高了 ANN 模型的決定係數 (R²)，在預測產品重量方面達到 98.51%，在能耗方面達到 98.87%。層的有意凍結似乎在提高模型效率和準確性方面起著更關鍵的作用。

Injection molding (IM) is one such complex manufacturing process characterized by nonlinear behavior. Unlike classic linear modeling techniques like simple regression, many machine learning models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. Artificial Neural Networks (ANN), specifically, have demonstrated exceptional performance in problems involving nonlinear modeling. Implementing ANN for predictive modeling in injection molding encounters notable obstacles due to the considerable volume of training data needed. Transfer learning (TL) provides a viable answer by utilizing knowledge from one dataset to improve the performance of a model on another dataset. The objective of this study is to utilize TL in order to forecast the energy consumption and product weight in injection molding by employing ANN. To acquire energy consumption statistics, it is necessary to conduct experiments directly. Thus, this work will employ complete factorial Design of Experiments (DoE) to acquire a dataset that is both resilient and suitable for training, validation, and testing purposes. When it comes to estimating weight and energy consumption for various materials, studies have shown that the use of transfer learning approach greatly speeds up the learning process. Empirical evidence has demonstrated that employing transfer learning technique significantly improves the coefficient of determination (R²) of the ANN model, reaching values of 98.51% in predicting product weight and 98.87% in energy consumption. The deliberate freezing of layers seems to have a more crucial role in enhancing the efficiency and accuracy of the model.

Table of Contents

摘要 i
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
List of Tables viii
Chapter I Background and Motivation 1
1.1 Background and Motivation 1
1.2 Problem Statement 2
1.3 Research Aim and Objective 3
1.4 Research Boundary 3
1.5 Research Position and Contribution 4
1.6 Outline of Thesis 5
Chapter II Literature Review 10
2.1 Injection Molding Machine 10
2.1.1 Product Weight 12
2.1.2 Energy Consumption 13
2.1.3 Melt Temperature 14
2.1.4 Mold Temperature 15
2.1.5 Injection Speed 16
2.1.6 Packing Pressure 17
2.1.7 Screw Speed 18
2.1.8 Packing Time and Cooling Time 19
2.1.9 Materials 20
2.2 Data Processing 23
2.2.1 Normalization 23
2.3 Machine Learning 24
2.3.1 Supervised Learning 25
2.3.2 Unsupervised Learning 27
2.4 Artificial Neural Networks 28
2.4.1 Layer and Neurons 30
2.5 Keras and TensorFlow 31
2.5.1 Transfer Learning 32
Chapter III Research Methodology 34
3.1 Problem Observation and Literature Review 35
3.2 Model Formulation 35
3.3 Data Collection 36
3.3.1 Experimental Equipment 36
3.3.2 Experimental Setup 40
3.4 Result and Analysis 40
3.5 Conclusion 41
Chapter IV Model Development 42
4.1 Experimental Design 43
4.2 Data Processing 46
4.3 Building ANN Model 51
4.3.1 ANN Base Model 54
4.3.2 ANN Transfer Learning Model 58
Chapter V Result and Analysis 59
5.1 Experiment Result 59
5.2 Base ANN Model 62
5.2.1 Result of 5 Input Base Model 62
5.2.2 Result of 7 Input Base Model 64
5.3 Transfer Learning ANN Model 67
5.3.1 Result of 5 Input Transfer Learning Model 67
5.3.2 Result of 7 Input Transfer Learning Model 73
5.4 Effect of Transfer Learning 76
5.5 Implications 79
Chapter VI Conclusion 81
6.1 Conclusion 81
6.2 Future Research 81
Bibliography 83


List of Figures
Figure 1 ANN learning flow diagram with Transfer Learning Method 3
Figure 2 Injection Molding Machine 10
Figure 3 Injection Molding Stage 11
Figure 4 Energy Consumption of Injection Molding Machine 13
Figure 5 Recommended Temperature Setting 15
Figure 6 Setting of Screw RPM 18
Figure 7 Relation Between Packing Time and Part Weight 19
Figure 8 Mechanical Properties Comparison PP 7533 and PA 756 20
Figure 9 Specific Heat Capacity Comparison PP 7533 and PA 756 21
Figure 10 Viscosity vs Shear Rate 22
Figure 11 Processing Temperature Comparison PP 7533 and PA 756 22
Figure 12 Transfer Learning Illustration 32
Figure 13 Research Flow Chart 34
Figure 14 Injection Molding Machine FCS HT-150SV 36
Figure 15 Mold Temperature Controller Device JSW-4018E 37
Figure 16 Analytical Balance HZK-FA210 38
Figure 17 Arduino Based – Power Meter Prototype (a) Arduino Mega and 3 16 bit ADS 1115 for DAQ (b) Current probe on Mold Temperature Controller Device (c) Current Probe on Melt Temperature Controller and Motor Controller Device 39
Figure 18 CENTER 206 Datalogger Dual Input Thermometer 39
Figure 19 Model Development 42
Figure 20 Schematic view of part product 43
Figure 21 Correlation Matrix from Preliminary Experiment 44
Figure 22 Code for Digital Low Pass Filter 46
Figure 23 Motor Controller Current Data 46
Figure 24 Melt Temperature Controller Current Data 47
Figure 25 Mold Temperature Controller Current Data 47
Figure 26 Data loss from filtering 47
Figure 27 Focus Observation for Motor Energy Consumption 48
Figure 28 Code for Data Split 49
Figure 29 Distribution of 486 Data 49
Figure 30 Code for Data Normalization 50
Figure 31 Normalization for input variable 51
Figure 32 Normalization for response 51
Figure 33 Illustration of activation function 53
Figure 34 Different Activation Response 54
Figure 35 Code For Hyperparameter Optimization using gp_minimize 55
Figure 36 7 Control Parameter for Base Model 57
Figure 37 Example of Training and Validation Loss Result 58
Figure 38 Correlation Matrix Experiment 1 60
Figure 39 Correlation Matrix Experiment 2 60
Figure 40 Correlation Matrix Combined Data 61
Figure 41 Illustration of 5 input base ANN model 62
Figure 42 Predict vs True Value 5 Input Base Model 63
Figure 43 Distribution Error 5 Input Base Model 63
Figure 44 Transfer Learning Model Illustration from 5 input to 7 input 64
Figure 45 Illustration of 7 input base ANN model 64
Figure 46 Predict vs True Value 7 Input Base Model 65
Figure 47 Distribution Error 7 Input Base Model 66
Figure 48 Transfer Learning Combination Method 67
Figure 49 Distribution Error of Weight Prediction 5 Input Transfer Learning Model 69
Figure 50 Distribution Error of Energy Consumption 5 Input Transfer Learning Model 70
Figure 51 Distribution Error of Weight Prediction 7 Input Transfer Learning Model 73
Figure 52 Distribution Error of Energy Consumption 7 Input Transfer Learning Model 74
Figure 53 Comparison of Loss Graph of (a) Conventional (b) 5 Input TL (c) 7 Input TL model 77

List of Tables
Table 1 Research State of The Art 7
Table 2 FCS HT-150SV Specification 37
Table 3 JSW-4018E Specification 37
Table 4 HZK-FA210 Specification 38
Table 5 Specification of CENTER 206 Datalogger Dual Input Thermometer 39
Table 6 Design of Experiment Taguchi L27 for preliminary 44
Table 7 Design of Experiment Full Factorial 243 45
Table 8 Design of Experiment CCD 158 46
Table 9 Hyperparameter tuning search space and results for 5 input base ANN model 56
Table 10 Hyperparameter tuning search space and results for 7 input base ANN model 57
Table 11 Hyperparameter tuning search space and results for 5 input TL ANN model 58
Table 12 Performance Comparison of Base Model 66
Table 13 Performance Comparison of 5 Input Transfer Learning Model 71
Table 14 Performance Comparison of 7 Input Transfer Learning Model 76
Table 15 5 Input TL Model Frozen Layer Effect 78
Table 16 7 Input TL Model Frozen Layer Effect 78
Table 17 Performance Comparison of 5 Input,7 Input Transfer Learning Model, and Conventional Model 78
 

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