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研究生： 林瑋玲
Lin, Wei-ling
論文名稱： 以蒙地卡羅法探討應變誘發之晶粒遷移
Investigation on Strain Induced Grain Migration Using Modified Monte Carlo Method
指導教授： 郭瑞昭
Kuo, Jui-Chao
學位類別： 碩士
Master
系所名稱： 工學院 - 材料科學及工程學系
Department of Materials Science and Engineering
論文出版年： 2007
畢業學年度： 95
語文別： 英文
論文頁數： 90
中文關鍵詞： 蒙地卡羅 、電腦模擬 、應變誘發晶界遷移
外文關鍵詞： Monte Carlo method, Strain-induced boundary boundary migration, computer simulation
相關次數： 點閱：61  下載：2 
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    • 為了探討應變誘發晶界遷移，本研究利用模擬以及實驗的方式來探討von Mises應變對晶界遷移方向的影響。在電腦模擬的部份，我們先對蒙地卡羅的模型作參數測試，並與理論值比較得到與實驗值相近的結果。在確認建立好的模型無誤後再將形變後以數位影像係數法(DIC)量測的2D不均勻von Mises應變數據為基礎建立新的蒙地卡羅方法。在實驗部分，以99.99%的純鋁為材料，在雙晶晶界不產生彎曲的狀況下對試片做5%的形變避免由曲率為驅動力造成的晶界遷移，接下來以30分鐘為單位在450℃的溫度中退火，觀察退火後的晶相。
    我們將退火結果與數位影像相關係數測量法分析出的von Mises應變分布做比較，再比對模擬與實驗結果，釐清晶界移動方向與移動速率，成功的模擬出與實驗相近的微結構分布圖。實驗結果顯示，在von Mises梯度較大的領域晶界遷移的速度愈快，應變儲存能也較大。

    During annealing, recrystallization and grain growth result in the microstructure evolution of alloys through grain refinement, texture development and relief of deformation stresses. A number of previous investigations have been carried out to understand the kinetics of recrystallization and grain growth in a variety of industrial applications. Until now there is no numerical model which can be used to predict kinetics of recrystallization (grain boundary migration) and textures.
    Knowledge of the heterogeneous stored energy plays a critical role at understanding the microstructure physics of strain-induced grain boundary migration or recrystallization during annealing the deformed materials. A lot of previous studies have been proposed to evaluate the stored energies of the deformed state. It is still an open question what is the influence of the stored energy on the strain-induced grain boundary migration. Therefore, the purpose of the work is to develop a physics-based model that can predict the strain-induced grain boundary migration.
    In this study a DIC-based Monte Carlo technique and experimental approaches were employed to understand kinetics of grain boundary in aluminum bicrystals, namely the dependence of microstructure development. In order to stimulate the strain-induced grain migration, an aluminum bicrystals was strained by using channel-die compression to 5% deformation. the microstrain distribution was determined by using digital image correlation (DIC) technique. The microstructures of the deformed bicrystals were investigated after annealing at 450℃ for 2 to 4 hours.
    The 2D strain full field provided data to simulate grain growth using a modified Monte Carlo method. The strain-induced grain growth on grain boundary was simulated and compared with experimental observations. From the direction of the grain boundary migration, it is observed that the larger gradient of von Mises strain results in the larger grain migration.

    中文摘要 I Abstract II 誌謝 IV Contents V Chapter 1 Introduction 1 2.1 Grain Growth 3 2.1.1 Introduction of Grain Growth 3 2.1.2 Kinetics Parameters of Grain Growth 6 2.1.3 Grain Size Distribution 9 2.2 Bridgman Method 10 2.3 Basic Concepts of Digital Image Correlation 15 2.3.1 Introduction of DIC 15 2.3.2 Determination of strain field 19 3.1. Introduction of Monte Carlo Method 24 3.2. Conventional Monte Carlo Method 27 3.2.1. Ising and Potts Model 27 3.2.2. Lattice type 28 3.2.4. The algorithm of conventional Monte Carlo method 34 3.3. A Fast Serial Algorithm of Monte Carlo Method 39 3.3.1. The 10-fold Way for Ising Model 40 3.3.2. A Fast Serial Algorithm for Potts Model 44 Chapter 4 Experiments and Numerical Procedure 49 4.1 Experiment Procedure 49 4.1.1 Materials 49 4.1.2 Channel Die Compression 52 4.1.3 Microstrain Measurement 54 4.1.4 Annealing Process 55 4.2 Numerical Procedure 56 Chapter 5 Results and Discussions 59 5.1 Numerical Test of Simulation Procedure 59 5.1.1 Influence of Orientation Number 59 5.1.2 Grain Growth Exponent 69 5.1.3 Grain Size Distribution 70 5.1.4 Influence of Microstructure Elements 72 5.2 Effects of Microstrain on the Grain Boundary Migration 76 5.2.1 Effect of Deformation Heterogeneity 76 5.2.2 Effect of Plastic Stored Energy 79 5.2.3 Comparison Between Simulation and Experiments 82 Chapter 6 Conclusions 85 Reference 86

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