# 臺灣博碩士論文加值系統

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 本篇論文利用變動鄰居塔布搜尋法(VNTS)和決策樹演算法(Decision Tree Algorithm)去建構一個規則歸納系統(RIS)。將已經訓練完成的類神經模式，與變動鄰居塔布搜尋法相結合，來尋找射出成型機參數組合之最佳可行解，結果發現此演算法具有不錯的尋優能力。決策樹演算法則是利用一開始田口實驗中所收集的實驗數據加以分析與分類，從此數據中歸納出可以調控射出成型機台的規則。 在本論文實證案例中，發現到變動鄰居塔布搜尋法確實可以提供多組可生產射出成型良品的機台參數。再利用這多組中最佳的機台參數來修正原始田口實驗中的參數水準，最後便由決策樹演算法使用此修改後的資料來歸納出可供調機者日後調機的規則。實驗結果發現本論文所歸納出來的規則與過去相關射出成型文獻所歸納出來的規則幾乎相近，只有少數幾條規則例外，判斷應該是所使用的射出材料與機台類型各自不同有關。由此看來，本論文可針對射出成型機台，提供了實用的調機規則與最佳參數，以減少不良品的嚴重程度與數目，未來也將有效地幫助未來其他精密機械等產業，降低其調機之成本與時間。
 In the thesis, variable neighborhood tabu search (VNTS) and Decision Tree Algorithm are used to implement the rule induction system (RIS). VNTS with the well-trained BPN criterion demonstrates good searching capability to find the best solution for parameter setting of injection molding machine. Decision Tree Algorithm is the tool for inducting rules from the experimental data. Taguchi method is adopted to perform task of experiment design. The illustrated examples in the empirical study show how this RIS works and sets up rules for machines. The rules inducted by Decision Tree Algorithm will be revised according to the best solution found by VNTS. The results show not only are the rules found by RIS pretty close to what the literature did, but also can RIS provide both the best parameters of injection molding machines and the appropriate machine operation rules to work staff in the workspace.
 Table of Contents摘要 IAbstract IIAcknowledgements IIITable of Contents IVList of Tables VList of Figures VIIChapter 1 Introduction 11.1 Research Background and Motivation 11.2 Research Purposes 61.3 Research Method 61.4 Research Process 7Chapter 2 Literature Review 92.1 Artificial Intelligence 92.1.1 Neural Network 92.1.2 Type of Neural Network 102.1.3 Back-Propagation Network 122.1.4 Variable Neighborhood Tabu Search 152.1.5 Decision Tree Algorithm 172.2 Injection Molding Manufacturing 17Chapter 3 Research Structure and Methods 213.1 Experimental Data 213.2 Combination Structure of Back-propagation network and Variable Neighborhood Tabu Search 213.3 Structure of Decision Tree Algorithm 293.4 Structure of the Entire System 32Chapter 4 Empirical Study 354.1 Training of Back-Propagation Network Criterion 354.2 The experimental implementation of VNTS optimization 404.2.1 The comparison of VNTS and TS 484.2.2 The other discovery of thesis 494.3 The implementation of decision tree algorithm 51Chapter 5 Conclusions and Contributions 675.1 Conclusions 675.2 Contribution 675.3 Future Research 68Reference 69Appendix The defect conditions of injection molding (IM) product 72List of TablesTable 1.1 The causes and solution policies of the defects 3Table 2.1 Summary of literature review 20Table 3.1 The operational and its level parameters 21Table 3.2 The experiment al combinations of L18 orthogonal array 21Table 3.3 The grading and weighting procedure of defects 23Table 4.1 The operational and its level parameters 36Table 4.2 The experimental combinations of L18 orthogonal array 36Table 4.3 The normalized experimental combinations of BPN inputs 37Table 4.4 The normalized experimental combinations of BPN outputs 38Table 4.5 The experimental results of VNTS with the level of iteration number of VNS is 10 and variable levels of iteration number of tabu 41Table 4.6 The experimental results of VNTS with the level of iteration number of VNS is 50 and variable levels of iteration number of tabu 41Table 4.7 The experimental results of VNTS with the level of iteration number of VNS is 100 and variable levels of iteration number of tabu 42Table 4.8 The experimental results of VNTS with the level of iteration number of VNS is 200 and variable levels of iteration number of tabu 42Table 4.9 The experimental results of VNTS with the level of iteration number of VNS is 500 and variable levels of iteration number of tabu 43Table 4.10 The experimental results of VNTS with the level of iteration number of VNS is 1000 and variable levels of iteration number of tabu 43Table 4.11 The experimental analysis of ANOVA to decide the best iteration combination of VNTS for the case 44Table 4.12 The V50T200 experimental results of 10 execution times 47Table 4.13 The experimental results of 10 execution times 48Table 4.14 The solution replacement time among the neighborhood of VNTS 49Table 4.15 The solution performance and experimental time after disusing the neighborhood of 2-swap with appointment. 50Table 4.16 The original setting of nominal orthogonal array 51Table 4.17 The new setting of the nominal orthogonal array based on the best solution found by VNTS 52Table 4.18 The original nominal data sets 53Table 4.17 Correct rate for the prediction of decision tree model for each kind of quality characteristic 54Table 4.19 The table of comparison results with other past literatures 63Table 4.20 The rearranged setting rules for the specific defect degree 65List of FiguresFig. 1.1 The injection molding machine [16] 1Fig1.2 Research framework 8Fig 2.1 Network structure of back-propagation network 12Fig 2.2 Neighborhood structures in VNTS 16Fig 3.1 Procedure of BPN training 22Fig 3.2 Parameter encoding solution 25Fig 3.3 Maximum encoding range 25Fig 3.4 two-swap with appointment 26Fig 3.5 Random single point change 26Fig 3.6 Random two point change 27Fig 3.7 The optimization procedure of VNTS algorithm 28Fig 3.8 The example of decision tree 31Fig 3.9 The structure of the entire system 34Fig 4.1. The structure of BPN criterion 37Fig 4.2 The training result after 100 epochs 39Fig 4.3 The training result after 5000 epochs 39Fig 4.4 The training result after 10000 epochs 39Fig 4.5 The training result after 20000 epoch 39Fig. 4.6 The illustration of T with significant interaction respect to V 45Fig. 4.7 The illustration of V with significant interaction respect to V 45Fig 4.8 The illustration of V50T200 experimental results of 10 execution times 47Fig 4.9 The data set for Clementine V6.5 53Fig 4.10 The decision tree model of Clementine V6.5 53Fig 4.11 The rules from decision tree of Clementine V6.5 for short shot 54Fig 4.12 The rules from decision tree of Clementine V6.5 for sink mark 56Fig 4.13 The rules from decision tree of Clementine V6.5 for Warp 58Fig 4.14 The rules from decision tree of Clementine V6.5 for burn mark 59Fig 4.15 The rules from decision tree of Clementine V6.5 for flash 60Fig 4.16 The rules from decision tree of Clementine V6.5 for weld line 61I. The normal condition of IM product 72II. The short shot condition of IM product 72III. The warp and flash condition of IM product with weld line problem 73IV. The burn mark condition of IM product (see the place the arrow points) 73V. The close-up picture of Burn mark condition of IM product (see the place the arrow points) 74VI. The short shot and sink mark condition of IM product with weld line problem 74VII. The warp condition of IM product 75VIII. The serious short shot condition of IM product with deforming problem 75
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