臺灣博碩士論文加值系統

伸線製程是一種塑性變形過程，逐漸減小線材的直徑。在該過程中，拉絲以通過單個模具（單道次製程）或多個模具（多道次製程）。面積縮減比分佈是多道次製程設計的關鍵部分。模具的幾何設計也會影響產品品質和加工時的能量消耗。另外，反向拉力的施加可以顯著降低模具的最大壓力並且延長模具的使用壽命。在這項研究中，設計並優化了五道鋼（AISI 1022低碳鋼合金）拉拔工藝，直徑從5.5mm縮減到2.75mm。每道次的拉絲速度都在特別在有限元法中控制。採用田口正交陣列（L_16 (4^3)）建立不同有限元情況下的參數組合。在模擬完成特定通過後，人工神經網絡（ANN）用於訓練一般模型。然後，基於人工神經網絡（ANNs）生成的模型，採用遺傳算法（GA）尋找當道次的最佳參數組合。
本研究中有三個優化目標 - 1. 拉拔力，為了減少能量消耗。 2. 線材表面的最大軸向應力，以便維持產品品質。 3. 模具上的最大應力，它會影響模具的磨損。所以，這是一項多目標的優化。其中，Meta模型的概念已經付諸實踐，因為有限元素法取代了現實世界的實驗，然後訓練了ANN模型以找到有限元素法的通解。在優化過程中，由於線材的不均勻變形，優化過程必須從第一道次開始，在針對之後的道次繼續。需要固定先前通過的參數以產生類似的非均勻變形和殘餘應力分佈；如果先前道次的參數改變，則會出現不同的模擬結果。最後，針對直進式伸線機，本研究發展出了一種機制來執行優化的結果。

Wire drawing is a plastic deformation process to gradually reduce the diameter of the wire rod. During the process, a wire is drawn to pass through either a die (single-pass process) or a series of dies (multi-pass process). Reduction ratio distribution is a crucial part of design in multi-pass process. Geometric designs of dies also affect the quality of product and the cost of energy. Additionally, the application of back tension can substantially reduce the maximum pressure of dies and incidentally extend service life of dies. In this research, a five passes steel (AISI 1022 Alloy) drawing process, from 5.5mm to 2.75mm in diameter, is designed and optimized. The drawing velocities in every pass were deliberately controlled in Finite Element Method. Taguchi Orthogonal arrays (L_16 (4^3)) was adopted to build up the parameter combination in different FEM cases. Artificial Neural Networks (ANNs) were used to train a general model after simulations were all finish in the specific pass. Then, Genetic Algorithm (GA) was employed to find the best combination of parameters in the specific pass based on the model generated by Artificial Neural Networks (ANNs). There were three optimization targets in this study— 1. The drawing force, relative to the cost of energy. 2. The maximum axial stress on wire surface, to maintain decent quality of product. 3. The maximum stress on dies, which influences the wear of dies. For instance, it is a multiple objective task. In general, the concept of Meta Model was taken into practice since the FEM was substituted for the real-world experiments and then the ANN model was trained to find a general solution of FEM. The process of optimization must start from the first to the fifth pass in sequence due to the nonuniform deformation. The parameters of previous passes need to be fixed in order to create the similar nonuniform deformation and residual stress distribution. In case the parameters of previous passes alter, different results appear. At last, a mechanism was invented to operate the optimization results.

Table of Contents
Abstract II
中文摘要 IV
致謝 VI
List of Tables XI
List of Figures XIV
Chapter 1 Introduction 1
1.1 Preface 1
1.1.1 The mechanism of wire drawing 1
1.2 Literature review 3
1.2.1 Experiments and simulations 3
1.2.2 Artificial intelligence adopted in wire drawing 8
1.3 Motivation of research 10
Chapter 2 Theory and Method 11
2.1 Factory case description 11
2.2 Rules of experience in designating reduction ratios for multi-pass process 11
2.2.1 Introduction of basic parameters and formulas 11
2.2.2 Steps of distributing the reduction ratios to each pass 12
2.3 Optimization methodology 14
2.4 Parameters and objectives of optimization system 16
2.4.1 Three parameters in the reduction angle, bearing length, and back tension 17
2.5 Optimization objectives 19
2.5.1 Drawing force analysis 19
2.5.2 Wire stress analysis 20
2.5.3 Die stress analysis 21
Chapter 3 Optimization techniques 23
3.1 Meta model 23
3.1.1 Orthogonal test table from Taguchi method 24
3.1.2 Neural Network 26
3.2 Genetic Algorithm 29
Chapter 4 Simulation settings 33
4.1 The software of simulation 33
4.2 Basic assumptions 34
4.2.1 AISI 1022 Low Carbon Steel 35
4.2.2 Coefficient of Coulomb friction 39
4.2.3 Wire drawing velocity 40
4.3 Mesh convergence study 41
42
Chapter 5 Result of simulation and optimization 44
5.1 Results of the 1st pass 44
5.1.1 16 results of simulation 44
5.1.2 Introduction of ANOVA test 45
5.1.3 Outcomes after ANOVA test 46
5.1.4 NN-GA optimization 48
5.2 Results of the 2nd pass 51
5.2.1 16 results of simulation 51
5.2.2 Outcomes after ANOVA test 52
5.2.3 NN-GA optimization 55
5.3 Results of the 3rd pass 57
5.3.1 16 results of simulation 57
5.3.2 Outcomes after ANOVA test 58
5.3.3 NN-GA optimization 61
62
5.4 Results of the 4th pass 64
5.4.1 16 results of simulation 64
5.4.2 Outcomes after ANOVA test 65
5.4.3 NN-GA optimization 67
5.5 Results of the 5th pass 69
5.5.1 16 Results of simulation 69
5.5.2 Outcomes after ANOVA test 70
5.5.3 NN-GA optimization 72
Chapter 6 Application of optimization results upon wire drawing machine 75
6.1 Brief explanation of the mechanism 75
6.2 Mechanical system of the optimization results 78
Chapter 7 Conclusions 81
Reference 82

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