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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80970完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊嘉揚(Jia-Yang Juang) | |
| dc.contributor.author | Ya-Hsuan Liao | en |
| dc.contributor.author | 廖亞軒 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:24:11Z | - |
| dc.date.available | 2021-09-13 | |
| dc.date.available | 2022-11-24T03:24:11Z | - |
| dc.date.copyright | 2021-09-13 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-08 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80970 | - |
| dc.description.abstract | 因為3D列印技術的發展，讓仿生複合材料得以跳脫傳統複合材料的製造與設計限制，師法自然界而設計出複雜的複合材料結構，而材料的優化方向也從剛性的強化轉向韌性的提升。也因為仿生複合材料的設計多樣性，機器學習的使用可以降低探索設計空間時所需的人力和時間。現有關於仿生複合材料的研究不勝枚舉，但鮮少有研究提出的從複合材料性質的有限元素模擬、實驗驗證再到機器學習應用相結合的完整方案，這亦是本研究欲達成之目標。 本研究設計一由兩種軟硬材料組合而成的平面網格複合材料，藉由加入少量的軟材料於硬材料中，在不影響其勁度表現的前提下，可以讓複合材料的韌度表現提到提升。本研究引用能量釋放速率和臨界能量釋放速率作為韌度的比較依據，以降低有限元素模擬所需的計算效能和時間，並透過Polyjet 3D列印技術製備試片進行實驗，並確認斷裂韌性與韌度間的關係。本研究也成功建立一套由卷積神經網路和基因演算法組合而成的反向設計方案，藉由卷積神經網路預測複合材料的韌度作為基因演算法的演化參考依據，設計出符合需求的平面網格複合材料。為相關複合材料設計提供一套簡單高效的開發方案。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:24:11Z (GMT). No. of bitstreams: 1 U0001-0709202113482500.pdf: 4480069 bytes, checksum: 9d0ebb46d66d8d817c330a120a0b0e7f (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "誌謝 I 摘要 II ABSTRACT III 目錄 V 圖目錄 VII 表目錄 IX 第一章 緒論 1 1.1 複合材料簡介 1 1.2 文獻回顧 2 1.3 研究動機與目的 4 第二章 相關理論 6 2.1 類神經網路(Neural networks, NN) 6 2.1.1 卷積神經網路(Convolutional neural networks, CNN ) 9 2.2 基因演算法(Genetic algorithm, GA ) 13 2.3 材料勁度(Stiffness)與韌度(Toughness ) 16 2.4 能量釋放速率(Energy release rate) 17 2.5 數位影像相關法(Digital image correlation, DIC) 22 第三章 研究方法與材料 27 3.1 實驗架構 27 3.2 有限元素模擬 28 3.2.1 複合材料設計 28 3.2.2 模擬方法與流程 29 3.2.3 大量模擬流程 32 3.3 試片材料與製備 35 3.3.1 熱熔融層積式3D列印(Fused deposition modeling, FDM) 35 3.3.2 彩色噴墨式3D列印(Polyjet) 38 3.4 拉伸試驗 39 3.4.1 數位影像相關法之開源軟體Ncorr操作 41 3.4.2 純材料性質量測 44 3.4.2.1 楊氏模數(Young’s modulus) 44 3.4.2.2 臨界能量釋放速率(Critical energy release rate) 44 3.4.3 複合材料性質量測 45 3.4.3.1 勁度量測 45 3.4.3.2 韌度量測 46 3.5 機器學習 47 3.5.1 機器學習軟體套件 47 3.5.2 反向設計模型架構 49 3.5.2.1 CNN預測複合材料性質模型 49 3.5.2.2 基因演算法設計複合材料模型 50 第四章 結果與討論 52 4.1 純材料性質 52 4.2 複合材料模擬與實驗結果之比較 54 4.2.1 勁度 57 4.2.2 韌度 58 4.3 反向設計 63 4.3.1 複合材料性質之預測結果 63 4.3.2 複合材料之設計結果 64 第五章 結論與未來展望 70 5.1 結論 70 5.2 未來展望 73 參考文獻 74" | |
| dc.language.iso | zh-TW | |
| dc.subject | 3D列印 | zh_TW |
| dc.subject | 複合材料 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 能量釋放速率 | zh_TW |
| dc.subject | 反向設計 | zh_TW |
| dc.subject | composite material | en |
| dc.subject | 3D printing | en |
| dc.subject | energy release rate | en |
| dc.subject | inverse design | en |
| dc.subject | genetic algorithm | en |
| dc.subject | convolutional neural network | en |
| dc.title | 以基因演算法設計平面網格複合材料 | zh_TW |
| dc.title | Design of Planar Grid Composite Materials Using Genetic Algorithm | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 劉益宏(Hsin-Tsai Liu),蔡佳霖(Chih-Yang Tseng) | |
| dc.subject.keyword | 複合材料,卷積神經網路,基因演算法,能量釋放速率,反向設計,3D列印, | zh_TW |
| dc.subject.keyword | composite material,convolutional neural network,genetic algorithm,inverse design,energy release rate,3D printing, | en |
| dc.relation.page | 83 | |
| dc.identifier.doi | 10.6342/NTU202103029 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-09 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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