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研究生: 柯采妏
Ko, Tsai-Wen
論文名稱: 人工智慧設計之旋性與非旋性固體的力學行為
MECHANICAL BEHAVIOR OF CHIRAL AND NON-CHIRAL ARCHITECTED SOLIDS DESIGNED BY ARTIFICIAL INTELLIGENCE
指導教授: 王雲哲
Wang, Yun-Che
學位類別: 碩士
Master
系所名稱: 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 121
中文關鍵詞: 彈性力學黏彈力學塑性力學階級材料負普松比負熱膨脹係數潛變相場模擬裂紋
外文關鍵詞: Elasticity, Viscoelasticity, Plasticity, Hierarchical structure, Negative Poisson’s ratio (NPR), Negative thermal expansion coefficient (NTEC), Creep, Phase field modeling, Crack
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  • 本文透過人工智慧方式生成一系列之二維旋性與非旋性模型,為了計算模型之彈性及黏彈性質,使用有限元素法來進行研究及討論。對於微觀結構參數的影響,計算旋性及非旋性結構在不同骨幹長度下,其楊氏模數、柏松比、體積模數、剪力模數的變化來進行觀察,當l與r之比值為1時,無負普松現象,當l與r之比值為1.5、正規化角度為0.8及l與r之比值為2、正規化角度為0.8,開始出現負普松(NPR)現象,顯示出當骨幹長度增長時,結構會提早進入負普松現象,於旋性結構中,張力與彎曲的耦合變形會被觀察到,另外,階級結構亦計算了前述之彈性性質,當階級從一增加至三時,可提升其楊氏模數,由17.5 kPa 提升至19.02 kPa,普松比由-0.26降低至-0.36。在負熱膨脹係數(NTEC)結構中,探討骨幹之曲率對熱膨脹係數及普松比影響,當結構為負熱膨脹情況時,曲率越大時,負熱膨脹係數及普松比越小,曲率由0.09提升至0.2 cm^-1時,其負熱膨脹係數由-1.49x10^6降至-9.6x10^5 K^-1,普松比由-0.72降至-0.98。此外,本論文對於蜂巢結構在潛變作用下,觀察其普松比非單調之變化。亦透過相場模擬,探討裂紋在填充式蜂巢結構之走向,當普松比越接近0.5時,可減緩裂紋之生長,當普松比越接近-1時,加速了裂紋生長。

    An artificial intelligence technique is adopted to create a series of chiral or non-chiral materials in two dimensions, identified by their geometric features. In order to characterize the effective elastic and viscoelastic properties of the chiral samples, corresponding finite element models are constructed. Effective Young’s modulus, Poisson’s ratio, bulk modulus, biaxial shear modulus, pure shear modulus and simple shear modulus are calculated to monitor the effects of microstructural parameters. It is found that negative effective Poisson’s ratio can be achieved in certain microstructural settings. When the ratio of l to r is 1, there is without negative Poisson’s ratio (NPR) phenomenon. But, when the ratio of l to r is 1.5, the normalized angle ratio is 0.8 and the ratio of l to r is 2, the normalized angle ratio is 0.8, they appear NPR phenomenon. Tension-bending deformation coupling is quantified in chiral materials. Hierarchical effects
    on the effective properties are analyzed. When the rank changes from 1 to 3, it can increase its Young’s modulus. The Young’s modulus changes from 17.5 kPa to 19.02 kPa and Poisson’s ratio becomes -0.26 to -0.36. For negative thermal expansion coefficient (NTEC) structures, we discuss that the curvature of skeleton effects on TEC and PR. When the structures are already under NTEC situation, greater curvature can cause more negative TEC and PR. The NTEC decreases from -1.49x10^6 to -9.6x10^5 K^-1 and Poisson’s ratio decreases -0.72 to -0.98 while curvature increase from 0.09 to 0.2 cm^-1. In addition, we analyze non-monotonic time-dependent Poisson’s ratio under creep in re-entrant cell honeycombs. Crack propagation in re-entrant cell honeycomb composites is investigated via the phase-field modeling. When the Poisson’s ratio is close to 0.5, it can decrease the crack propagation. On the contrary, when the Poisson’s ratio is close to -1, it can increase the crack propagation.

    CHINESE ABSTRACT i ABSTRACT ii ACKNOWLEDGMENTS iii LIST OF TABLES vii LIST OF FIGURES ix NOMENCLATURE xv 1 Introduction 1 1.1 Goals and motivation 1 1.2 Literature review 2 1.2.1 Artificial intelligence 2 1.2.2 Chiral structures 2 1.2.3 Hierarchical structures 2 1.2.4 Negative thermal expansion coefficient 3 1.3 Outline of this thesis 3 2 Artificial intelligence 4 2.1 Theory 4 2.1.1 Deep Neural Networks 4 2.1.2 Generative Adversarial Network 5 2.1.3 Markov chain 5 2.2 Chiral and non-chiral structures designed by artificial intelligence 6 3 Theoretical aspects 7 3.1 Elasticity 7 3.1.1 Elasticity analysis in two-dimensional 7 3.1.2 Effects of thermal expansion 9 3.2 Plasticity 10 3.2.1 The criterion of isotropic system 10 3.2.2 Creep deformation 11 3.3 Viscoelasticity 11 3.4 Phase field model of crack 13 4 Numerical information 16 4.1 Finite element basis 16 4.2 Model description 17 4.2.1 Chiral and non-chiral structures under different length between nodes 17 4.2.2 Hierarchical chiral and and non-chiral structures 24 4.2.3 Hierarchical chiral and re-entrant structures 27 4.2.4 Negative thermal expansion coefficient structures 30 4.2.5 Re-entrant cell honeycombs 32 4.2.6 Phase field model 34 5 Results and discussion 36 5.1 Mechanical properties of chiral and non-chiral structures 36 5.1.1 Effective properties of chiral and non-chiral structures under different length between nodes 36 5.1.2 Effective properties of hierarchical chiral and non-chiral structures 40 5.1.3 Bending of the cantilever chiral and non-chiral structures 57 5.2 Viscoelastic properties with hierarchical structures 64 5.2.1 Chiral structures 64 5.2.2 Re-entrant structures 68 5.3 Negative thermal expansion coefficient with re-entrant cell honeycombs 72 5.3.1 Thermal expansion coefficient of the microstructures 72 5.3.2 The thermal expansion coefficient of the models with different Young’s modulus 76 5.3.3 The Poisson’s ratio of the models 78 5.4 Creep effect on PPR and NPR re-entrant cell honeycombs 80 5.5 Phase field of crack effect on filled material with different Poisson’s ratio 84 6 Conclusion and future work 91 6.1 Conclusion 91 6.2 Future work 93 LIST OF REFERENCES 94 APPENDICES Appendix A: Python code of ligament growing 96 Appendix B: Presentation slides 103 Index 121

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