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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊嘉揚(Jia-Yang Juang) | |
| dc.contributor.author | Yi-Xian Xu | en |
| dc.contributor.author | 徐亦賢 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:41:07Z | - |
| dc.date.available | 2021-08-06 | |
| dc.date.available | 2022-11-24T03:41:07Z | - |
| dc.date.copyright | 2021-08-06 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-23 | |
| dc.identifier.citation | [1] E. Song, J. Li, S. M. Won, W. Bai, and J. A. Rogers, 'Materials for flexible bioelectronic systems as chronic neural interfaces,' Nature Materials, vol. 19, no. 6, pp. 590-603, 2020. [2] R. Ghaffari et al., 'Soft wearable systems for colorimetric and electrochemical analysis of biofluids,' Advanced Functional Materials, vol. 30, no. 37, 2019. [3] X. Ning et al., 'Assembly of advanced materials into 3D functional structures by methods inspired by origami and kirigami: A review,' Advanced Materials Interfaces, vol. 5, no. 13, 2018. [4] A. M. Abdullah, X. Li, P. V. Braun, J. A. Rogers, and K. J. Hsia, 'Kirigami‐inspired self‐assembly of 3D structures,' Advanced Functional Materials, vol. 30, no. 14, 2020. [5] C. Yu et al., 'Electronically programmable, reversible shape change in two‐and three‐dimensional hydrogel structures,' Advanced Materials, vol. 25, no. 11, pp. 1541-1546, 2013. [6] M. Aoki and J.-Y. Juang, 'Forming three-dimensional closed shapes from two-dimensional soft ribbons by controlled buckling,' Royal Society Open Science, vol. 5, no. 2, p. 171962, 2018. [7] C. Majidi, 'Soft robotics: A perspective—current trends and prospects for the future,' Soft Robotics, vol. 1, no. 1, pp. 5-11, 2014. [8] D. Rus and M. T. Tolley, 'Design, fabrication and control of soft robots,' Nature, vol. 521, no. 7553, pp. 467-75, 2015. [9] F. Schmitt, O. Piccin, L. Barbé, and B. Bayle, 'Soft robots manufacturing: A review,' Frontiers in Robotics and AI, vol. 5, 2018. [10] M. A. Skylar-Scott, J. Mueller, C. W. Visser, and J. A. Lewis, 'Voxelated soft matter via multimaterial multinozzle 3D printing,' Nature, vol. 575, no. 7782, pp. 330-335, 2019. [11] C.-Y. Yeh, C.-Y. Chen, and J.-Y. Juang, 'Soft hopping and crawling robot for in-pipe traveling,' Extreme Mechanics Letters, vol. 39, 2020. [12] C.-Y. Yeh, S.-C. Chou, H.-W. Huang, H.-C. Yu, and J.-Y. J. E. M. L. Juang, 'Tube-crawling soft robots driven by multistable buckling mechanics,' Extreme Mechanics Letters, vol. 26, pp. 61-68, 2019. [13] H. J. Qi and M. C. Boyce, 'Stress–strain behavior of thermoplastic polyurethanes,' Mechanics of Materials, vol. 37, no. 8, pp. 817-839, 2005. [14] L. Mullins, 'Softening of rubber by deformation,' Rubber Chemistry and Technology, vol. 42, no. 1, pp. 339-362, 1969. [15] J. Diani, B. Fayolle, and P. Gilormini, 'A review on the Mullins effect,' European Polymer Journal, vol. 45, no. 3, pp. 601-612, 2009. [16] A. Avanzini and D. Gallina, 'Effect of cyclic strain on the mechanical behavior of a thermoplastic polyurethane,' Journal of Engineering Materials and Technology, vol. 133, no. 2, 2011. [17] J. Yi, M. C. Boyce, G. F. Lee, and E. Balizer, 'Large deformation rate-dependent stress–strain behavior of polyurea and polyurethanes,' Polymer, vol. 47, no. 1, pp. 319-329, 2006. [18] Z. Ding, O. Weeger, H. J. Qi, and M. L. Dunn, '4D rods: 3D structures via programmable 1D composite rods,' Materials Design, vol. 137, pp. 256-265, 2018. [19] M. R. Garza, E. A. Peraza-Hernandez, D. J. Hartl, and H. E. Naguib, 'Self-folding origami surfaces of non-zero Gaussian curvature,' in Behavior and Mechanics of Multifunctional Materials XIII, 2019. [20] G. P. T. Choi, L. H. Dudte, and L. Mahadevan, 'Programming shape using kirigami tessellations,' Nature Materials, vol. 18, no. 9, pp. 999-1004, 2019. [21] W. Wang, 'Mechanical assembly of thermo‐responsive polymer‐based untethered shape‐morphing structures,' Macromolecular Materials and Engineering, vol. 305, no. 1, 2019. [22] E. Siefert, E. Reyssat, J. Bico, and B. Roman, 'Bio-inspired pneumatic shape-morphing elastomers,' Nature Materials, vol. 18, no. 1, pp. 24-28, 2019. [23] C. Baek, A. O. Sageman-Furnas, M. K. Jawed, and P. M. Reis, 'Form finding in elastic gridshells,' Proceedings of the National Academy of Sciences of the United States of America, vol. 115, no. 1, pp. 75-80, 2018. [24] B. An et al., 'Thermorph: Democratizing 4D printing of self-folding materials and interfaces.,' in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018, pp. 1-12. [25] Z. Ding, C. Yuan, X. Peng, T. Wang, H. J. Qi, and M. L. Dunn, 'Direct 4D printing via active composite materials,' Science Advances, vol. 3, no. 4, p. e1602890, 2017. [26] G. F. Hu, A. R. Damanpack, M. Bodaghi, and W. H. Liao, 'Increasing dimension of structures by 4D printing shape memory polymers via fused deposition modeling,' Smart Materials and Structures, vol. 26, no. 12, 2017. [27] W. M. van Rees, E. Vouga, and L. Mahadevan, 'Growth patterns for shape-shifting elastic bilayers,' Proceedings of the National Academy of Sciences of the United States of America, vol. 114, no. 44, pp. 11597-11602, 2017. [28] J. W. Boley et al., 'Shape-shifting structured lattices via multimaterial 4D printing,' Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 42, pp. 20856-20862, 2019. [29] R. Guseinov, C. McMahan, J. Perez, C. Daraio, and B. Bickel, 'Programming temporal morphing of self-actuated shells,' Nature Communications, vol. 11, no. 1, p. 237, 2020. [30] F. Momeni, S. M.Mehdi Hassani.N, X. Liu, and J. Ni, 'A review of 4D printing,' Materials Design, vol. 122, pp. 42-79, 2017. [31] J. Choi, O. C. Kwon, W. Jo, H. J. Lee, and M. W. Moon, '4D printing technology: A review,' 3D Printing and Additive Manufacturing, vol. 2, no. 4, pp. 159-167, 2015. [32] Z. Liu, D. Zhu, S. P. Rodrigues, K. T. Lee, and W. Cai, 'Generative model for the inverse design of metasurfaces,' Nano Letters, vol. 18, no. 10, pp. 6570-6576, 2018. [33] H. Aharoni, Y. Xia, X. Zhang, R. D. Kamien, and S. Yang, 'Universal inverse design of surfaces with thin nematic elastomer sheets,' Proceedings of the National Academy of Sciences of the United States of America, vol. 115, no. 28, pp. 7206-7211, 2018. [34] M. Mirza and S. Osindero, 'Conditional generative adversarial nets,' arXiv preprint, p. arXiv:1411.1784, 2014. [35] A. Radford, L. Metz, and S. Chintala, 'Unsupervised representation learning with deep convolutional generative adversarial networks,' arXiv preprint, 2015. [36] H. Reismann, Elasticity, theory and applications / Herbert Reismann, Peter S. Pawlik (no. Accessed from https://nla.gov.au/nla.cat-vn1203258). New York: Wiley, 1980. [37] B. McGinty. Green Almansi strains. Available: http://www.continuummechanics.org/greenstrain.html [38] R. Hill, 'Constitutive inequalities for isotropic elastic solids under finite strain,' Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, vol. 314, no. 1519, pp. 457-472, 1970. [39] H. Hencky, 'Uber die form des elastizitatsgesetzes bei ideal elastischen stoffen,' Zeitschrift für Physik, vol. 9, pp. 215-220, 1928. [40] M. A. Biot and D. C. Drucker, 'Mechanics of incremental deformation,' Journal of Applied Mechanics, vol. 32, no. 4, pp. 957-958, 1965. [41] P. Mott and C. J. P. r. B. Roland, 'Limits to Poisson’s ratio in isotropic materials,' Physical Review B, vol. 80, no. 13, p. 132104, 2009. [42] T. C. T. Ting and T. Chen, 'Poisson's ratio for anisotropic elastic materials can have no bounds,' The Quarterly Journal of Mechanics and Applied Mathematics, vol. 58, no. 1, pp. 73-82, 2005. [43] M. F. Beatty and D. Stalnaker, 'The Poisson function of finite elasticity,' (in English), Journal of Applied Mechanics-Transactions of the Asme, vol. 53, no. 4, pp. 807-813, 1986. [44] G. N. Greaves, A. L. Greer, R. S. Lakes, and T. Rouxel, 'Poisson's ratio and modern materials,' Nature Materials, vol. 10, no. 11, pp. 823-37, 2011. [45] L. A. Mihai and A. Goriely, 'How to characterize a nonlinear elastic material? A review on nonlinear constitutive parameters in isotropic finite elasticity,' Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 473, no. 2207, p. 20170607, 2017. [46] R. S. Lakes and A. Wineman, 'On Poisson’s ratio in linearly viscoelastic solids,' Journal of Elasticity, vol. 85, no. 1, pp. 45-63, 2006. [47] Y. Isono, 'Mechanical behavior of filled rubbers,' in Encyclopedia of polymeric nanomaterials, S. Kobayashi and K. Müllen, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, pp. 1210-1213. [48] J. Harwood, L. Mullins, and A. Payne, 'Stress softening in natural rubber vulcanizates. Part II. Stress softening effects in pure gum and filler loaded rubbers,' Rubber Chemistry and Technology, vol. 39, no. 4, pp. 814-822, 1966. [49] F. Bueche, 'Molecular basis for the Mullins effect,' Journal of Applied Polymer Science, vol. 4, no. 10, pp. 107-114, 1960. [50] R. Houwink, 'Slipping of molecules during the deformation of reinforced rubber,' Rubber Chemistry and Technology, vol. 29, no. 3, pp. 888-893, 1956. [51] D. E. Hanson et al., 'Stress softening experiments in silica-filled polydimethylsiloxane provide insight into a mechanism for the Mullins effect,' Polymer, vol. 46, no. 24, pp. 10989-10995, 2005. [52] Q. Zhao, H. J. Qi, and T. Xie, 'Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding,' Progress in Polymer Science, vol. 49-50, pp. 79-120, 2015. [53] C. A. Harper, Handbook of plastics, elastomers, and composites. McGraw-Hill Education, 2002. [54] S. Erkeçoglu, A. D. Sezer, and S. Bucak, 'Smart delivery systems with shape memory and self-folding polymers,' in Smart drug delivery system, 2016. [55] M. Bodaghi, A. R. Damanpack, and W. H. Liao, 'Triple shape memory polymers by 4D printing,' Smart Materials and Structures, vol. 27, no. 6, 2018. [56] M. Zare, M. P. Prabhakaran, N. Parvin, and S. Ramakrishna, 'Thermally-induced two-way shape memory polymers: Mechanisms, structures, and applications,' Chemical Engineering Journal, vol. 374, pp. 706-720, 2019. [57] Y. Wu et al., 'Two-way shape memory polymer with “switch–spring” composition by interpenetrating polymer network,' Journal of Materials Chemistry A: Materials for Energy and Sustainability, vol. 2, no. 44, pp. 18816-18822, 2014. [58] K. K. Westbrook et al., 'Two-way reversible shape memory effects in a free-standing polymer composite,' Smart Materials and Structures, vol. 20, no. 6, 2011. [59] A. J. W. McClung, G. P. Tandon, K. E. Goecke, and J. W. Baur, 'Non-contact technique for characterizing full-field surface deformation of shape memory polymers at elevated and room temperatures,' Polymer Testing, vol. 30, no. 1, pp. 140-149, 2011. [60] B. Pan, 'Digital image correlation for surface deformation measurement: Historical developments, recent advances and future goals,' Measurement Science and Technology, vol. 29, no. 8, 2018. [61] W. H. Peters and W. F. Ranson, 'Digital imaging techniques in experimental stress analysis,' Optical Engineering, vol. 21, no. 3, 1982. [62] J. Blaber, B. Adair, and A. Antoniou, 'Ncorr: Open-source 2D digital image correlation matlab software,' Experimental Mechanics, vol. 55, no. 6, pp. 1105-1122, 2015. [63] P. Lava, S. Cooreman, and D. Debruyne, 'Study of systematic errors in strain fields obtained via DIC using heterogeneous deformation generated by plastic fea,' Optics and Lasers in Engineering, vol. 48, no. 4, pp. 457-468, 2010. [64] N. A. Hoult, W. Andy Take, C. Lee, and M. Dutton, 'Experimental accuracy of two dimensional strain measurements using digital image correlation,' Engineering Structures, vol. 46, pp. 718-726, 2013. [65] B. Pan, L. Yu, J. Yuan, Z. Shen, and G. Tang, 'Determination of viscoelastic Poisson’s ratio of solid propellants using an accuracy-enhanced 2D digital image correlation technique,' Propellants, Explosives, Pyrotechnics, vol. 40, no. 6, pp. 821-830, 2015. [66] B. Pan, L. Yu, and D. Wu, 'High-accuracy 2D digital image correlation measurements using low-cost imaging lenses: Implementation of a generalized compensation method,' Measurement Science and Technology, vol. 25, no. 2, p. 025001, 2014. [67] F. Zhu, P. Bai, H. Shi, Z. Jiang, D. Lei, and X. He, 'Enhancement of strain measurement accuracy using optical extensometer by application of dual-reflector imaging,' Measurement Science and Technology, vol. 27, no. 6, 2016. [68] L. Yu and B. Pan, 'In-plane displacement and strain measurements using a camera phone and digital image correlation,' Optical Engineering, vol. 53, no. 5, 2014. [69] H. A. Alshahrani, 'Review of 4D printing materials and reinforced composites: Behaviors, applications and challenges,' Journal of Science: Advanced Materials and Devices, vol. 6, no. 2, pp. 167-185, 2021. [70] S. Tibbits, '4D printing: Multi-material shape change,' Architectural Design, vol. 84, no. 1, pp. 116-121, 2014. [71] Z. Liu, H. Liu, G. Duan, and J. Tan, 'Folding deformation modeling and simulation of 4D printed bilayer structures considering the thickness ratio,' Mathematics and Mechanics of Solids, vol. 25, no. 2, pp. 348-361, 2019. [72] A. Sydney Gladman, E. A. Matsumoto, R. G. Nuzzo, L. Mahadevan, and J. A. Lewis, 'Biomimetic 4D printing,' Nature Materials, vol. 15, no. 4, pp. 413-418, 2016. [73] L. Kačergis, R. Mitkus, and M. Sinapius, 'Influence of fused deposition modeling process parameters on the transformation of 4D printed morphing structures,' Smart Materials and Structures, vol. 28, no. 10, 2019. [74] A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, '2D and 3D face recognition: A survey,' Pattern Recognition Letters, vol. 28, no. 14, pp. 1885-1906, 2007. [75] X. Chenghua, W. Yunhong, T. Tieniu, and Q. Long, 'Depth vs. Intensity: Which is more important for face recognition?,' in Proceedings of the 17th International Conference on Pattern Recognition, 2004., 2004, vol. 1, pp. 342-345 Vol.1. [76] Y. Cai, Y. Lei, M. Yang, Z. You, and S. Shan, 'A fast and robust 3D face recognition approach based on deeply learned face representation,' Neurocomputing, vol. 363, pp. 375-397, 2019. [77] C. R. Qi, H. Su, K. Mo, and L. J. Guibas, 'PointNet: Deep learning on point sets for 3D classification and segmentation,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652-660. [78] A. S. Jackson, A. Bulat, V. Argyriou, and G. Tzimiropoulos, 'Large pose 3D face reconstruction from a single image via direct volumetric CNN regression,' in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1031-1039. [79] S. Moschoglou, S. Ploumpis, M. A. Nicolaou, A. Papaioannou, and S. Zafeiriou, '3DFaceGAN: Adversarial nets for 3D face representation, generation, and translation,' International Journal of Computer Vision, vol. 128, pp. 2534-2551, 2020. [80] A. Ioannidou, E. Chatzilari, S. Nikolopoulos, and I. Kompatsiaris, 'Deep learning advances in computer vision with 3D data: A survey,' ACM Computing Surveys, vol. 50, no. 2, 2017. [81] O. Kisi, M. Alizamir, and A. Docheshmeh Gorgij, 'Dissolved oxygen prediction using a new ensemble method,' Environmental Science and Pollution Research, vol. 27, no. 9, pp. 9589-9603, 2020. [82] D. Tran Anh, T. Duc Dang, and S. Pham Van, 'Improved rainfall prediction using combined pre-processing methods and feed-forward neural networks,' J, vol. 2, no. 1, pp. 65-83, 2019. [83] S. S. Kumar. (2019). Why sigmoid? Available: https://medium.com/n%C3%BAcleoml/why-sigmoid-ee95299e11fd [84] D. P. Kingma and J. Ba, 'Adam: A method for stochastic optimization,' arXiv preprint, 2014. [85] T. Huang. (2018). 機器/深度學習-基礎數學(三):梯度最佳解相關算法(gradient descent optimization algorithms). Available: https://reurl.cc/dGDYdy [86] W. Rawat and Z. Wang, 'Deep convolutional neural networks for image classification: A comprehensive review,' Neural Computation, vol. 29, no. 9, pp. 2352-2449, 2017. [87] Z. C. Lipton, J. Berkowitz, and C. Elkan, 'A critical review of recurrent neural networks for sequence learning,' arXiv preprint, 2015. [88] A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, 'Generative adversarial networks: An overview,' IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 53-65, 2018. [89] Y. Kim, C. Yang, Y. Kim, G. X. Gu, and S. Ryu, 'Designing an adhesive pillar shape with deep learning-based optimization,' ACS Applied Materials Interfaces, vol. 12, no. 21, pp. 24458-24465, 2020. [90] Z. Wang, S. Rosa, B. Yang, S. Wang, N. Trigoni, and A. Markham, '3D-physnet: Learning the intuitive physics of non-rigid object deformations,' arXiv preprint, 2018. [91] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, 'Image-to-image translation with conditional adversarial networks,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1125-1134. [92] I. J. Goodfellow et al., 'Generative adversarial networks,' arXiv preprint, 2014. [93] H.-Y. Lee. Generation. Available: https://speech.ee.ntu.edu.tw/~hylee/ml/ml2021-course-data/gan_v10.pdf [94] M. Arjovsky, S. Chintala, and L. Bottou, 'Wasserstein generative adversarial networks,' in International Conference on Machine Learning, 2017, pp. 214-223: PMLR. [95] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, 'Improved training of Wasserstein GANs,' arXiv preprint, 2017. [96] INPLUS.TW. 3d列印線材中的變形金剛. Available: https://inplus.tw/archives/5863 [97] S. T. Inc. Shape memory polymer │SMP technologies inc. Available: http://www.smptechno.com/index_en.html [98] Y. Yang, Y. Chen, Y. Wei, and Y. Li, '3D printing of shape memory polymer for functional part fabrication,' The International Journal of Advanced Manufacturing Technology, vol. 84, no. 9, pp. 2079-2095, 2016. [99] J. W. Gooch, 'ASTM D638,' in Encyclopedic dictionary of polymers, J. W. Gooch, Ed. New York, NY: Springer New York, 2011, pp. 51-51. [100] R. Zou et al., 'Isotropic and anisotropic elasticity and yielding of 3D printed material,' Composites, Part B: Engineering, vol. 99, pp. 506-513, 2016. [101] S. Rohde et al., 'Experimental characterization of the shear properties of 3D–printed ABS and polycarbonate parts,' Experimental Mechanics, vol. 58, no. 6, pp. 871-884, 2018. [102] J. Blaber. Ncorrmanual_v1_2_2. Available: http://www.ncorr.com/download/ncorrmanual_v1_2_2.pdf [103] J. Blaber. Applications_ncorr v1.2. Available: http://www.ncorr.com/index.php/applications [104] A. Khwaja, A. Asthana, and R. Goecke, 'Illumination and expression invariant face recognition using ssim based sparse representation,' in The 2010 International Conference on Pattern Recognition, 2010. [105] A. Dhall, A. Asthana, and R. Goecke, 'A SSIM-based approach for finding similar facial expressions,' in 2011 IEEE International Conference on Automatic Face Gesture Recognition, 2011, pp. 815-820. [106] c. Wikipedia, 'Joe biden,' in Wikipedia, The Free Encyclopedia., ed. [107] c. Wikipedia, 'Presidency of Donald Trump,' in Wikipedia, The Free Encyclopedia., ed. [108] L. Fei-Fei, R. Fergus, and P. Perona, 'One-shot learning of object categories,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, 2006. [109] Y. Guo and L. Zhang, 'One-shot face recognition by promoting underrepresented classes,' arXiv preprint, 2017. [110] G. Koch, R. Zemel, and R. Salakhutdinov, 'Siamese neural networks for one-shot image recognition,' in ICML Deep Learning Workshop, 2015, vol. 2: Lille. [111] N. W. Tschoegl, W. G. Knauss, and I. Emri, 'Poisson’s ratio in linear viscoelasticity – a critical review,' Mechanics of Time-Dependent Materials, vol. 6, no. 1, pp. 3-51, 2002. [112] D. Tscharnuter, M. Jerabek, Z. Major, and R. W. Lang, 'Time-dependent Poisson’s ratio of polypropylene compounds for various strain histories,' Mechanics of Time-Dependent Materials, vol. 15, no. 1, pp. 15-28, 2010. [113] M. Angelidi, A. P. Vassilopoulos, and T. Keller, 'Displacement rate and structural effects on Poisson ratio of a ductile structural adhesive in tension and compression,' International Journal of Adhesion and Adhesives, vol. 78, pp. 13-22, 2017. [114] L. Bashmal, Y. Bazi, H. AlHichri, M. M. AlRahhal, N. Ammour, and N. Alajlan, 'Siamese-GAN: Learning invariant representations for aerial vehicle image categorization,' Remote Sensing, vol. 10, no. 2, p. 351, 2018. [115] Y. Ge et al., 'FD-GAN: Pose-guided feature distilling GAN for robust person re-identification,' arXiv preprint, 2018. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81290 | - |
| dc.description.abstract | "本研究分為兩個方向，兩者皆以3D列印作為出發點，第一部分針對3D列印材料的性質精密量測作為目標；第二部分則針對3D列印材料於4D列印的應用進行新穎加工方法的探討，看似不存在關聯，但第一部分的內容為量測方法的探討，其結果可應用於第二部分的材料作為未來相關研究的使用。 隨著3D列印技術的普及，快速成型(Rapid prototyping)超彈性材料也成為3D列印不可或缺的特色之一，這也成為軟性機器人製造與設計時常用的方法。然而超彈性材料因類似橡膠的性質，使其存在週次軟化的現象，此現象對於機器人性能有顯著影響。由於超彈性材料在大變形下難以進行精確量測，使3D列印材料性質的研究多以硬材料為研究目標，且在週次軟化的研究中，並無針對其非線性蒲松比進行量測，因此本研究旨在精確量測3D列印超彈性材料的機械性質，其中透過量測熱塑性聚氨酯(TPU 85A, NinjaTek, Ninjaflex)於週次拉伸試驗中的機械性值了解週次軟化現象的非線性蒲松比變化。TPU 85A為現今熔融堆疊式3D列印中剛性最低的材料，其優異的彈性成為許多軟性機器人材料的首選。為了精確量測其性質，本研究使用平面影像關聯法搭配Reference sample compensation (RSC)修正方法來達到此目的，該方法能有效修正進出紙面位移帶來的量測誤差。在此修正方法下，成功觀測到蒲松比隨著週次軟化與遲滯變化的現象，在第一週次下蒲松比由較低的值0.45 ± 0.005提升至較高的值0.48 ± 0.005，而在後續週次下蒲松比維持在較高值且隨著拉伸應變的增長有些為變化，此蒲松比變化導致試片在最大拉伸應變為17.5%下體積有些微的增加(≈ 1%) 。此發現可協助有限元素法使用者進行軟性機器人之輔助設計，也有助於了解週次軟化現象的物理機制並提供其他研究來驗證週次軟化的理論模型。 4D列印為3D列印技術的延伸，係使用智能材料使3D列印的物件可透過外界刺激而變形，此機制可以應用於機器人或形狀變形等領域。形狀變形的優勢使其能透過簡單的結構轉換為複雜結構，克服3D列印對於複雜曲面印製的困難。過去研究未使用形狀記憶聚合物組成的平面網格透過4D列印來進行立體網格的製作，且反向設計其平面網格的過程困難且繁瑣。因此本研究希望以形狀記憶聚合物作為平面網格材料經4D列印的過程來做為立體網格的加工方法，並以人臉的理想模型系統(Model system)來測試此方法可行性，而立體網格的反向設計除了以人為設計外將搭配深度學習來加速該過程。而其中本研究透過熔融堆疊式3D列印機在列印智能材料(SMP55)時會在材料中殘留預應力的特性，找出一個能穩定列印且又能使SMP55能產生高達60%收縮率的列印參數，並透過與PLA組合的雙層結構使其能向上與向下彎曲，在這兩種材料四種組合配置形成的平面網格來達成4D列印。人為設計的部分藉由尋找平面網格不同設計下變形的規律，搭配有限元素法成功完成了三個日本能面的製作，也驗證了該方法於立體網格加工方法的可行性，且能藉由本研究設計的過程可將此技術應用於其他立體網格的製作。深度學習的部分，本研究將人臉面具的平面網格設計參數化來生成隨機的人臉面具，以此大量的隨機面具來進形人臉面具反向設計模型訓練的依據，其中條件式深層卷積生成對抗網路作為反向設計的模型架構，神經網絡根據目標的深度照片來生成平面網格設計，而其生成的人臉面具與目標的深度照片在結構相似性的計算下有77%的相似度，其結果仍存在一定的進步空間。在本研究網絡模型訓練的結果、參數化隨機人臉生成與4D列印平面網格列印的方法之上，未來能使反向設計的神經網絡模型更加完善。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:41:07Z (GMT). No. of bitstreams: 1 U0001-2307202112081600.pdf: 20291015 bytes, checksum: 6eb21c6c1772aaf49e76c0baf80495b1 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "誌謝 I 摘要 II ABSTRACT IV 目錄 VIII 表目錄 XII 圖目錄 XIII 符號表 XIX 第一章 緒論 1 1.1 研究動機與目的 1 1.1.1 3D列印之超彈性材料蒲松比精密量測 1 1.1.2 4D列印之平面網格變形──面具製作搭配反向設計 3 1.2 論文架構 5 第二章 相關理論與文獻回顧 6 2.1 固體力學理論 6 2.1.1 有限應變理論與無限小應變理論 6 2.1.2 非線性蒲松比 9 2.2 聚合物之機械性質 10 2.2.1 馬林斯效應與週次應力軟化 10 2.2.2 形狀記憶效應與玻璃轉化 12 2.3 影像關聯法(Digital image correlation, DIC) 15 2.4 4D列印 17 2.5 3D人臉模型表示 19 2.6 深度學習(Deep learning) 21 2.7 生成對抗網絡(Generative adversarial networks, GAN) 23 2.8 實驗室研究回顧 26 2.8.1 兼具跳躍與爬行之軟性爬管機器人 26 2.8.2 利用挫屈成形之薄壁封閉結構 27 第三章 實驗流程與使用工具材料 28 3.1 3D列印 28 3.2 實驗與製作材料 30 3.2.1 NinjaFlex 30 3.2.2 SMP55 (Shape Memory Polymer 55) 31 3.2.3 PLA 33 3.3 3D列印之超彈性材料蒲松比精密量測 34 3.3.1 實驗流程架構 34 3.3.2 試片製作 35 3.3.3 實驗架設 36 3.4 Ncorr 開源軟體 38 3.5 4D列印之平面網格變形──面具製作搭配反向設計 40 3.5.1 實驗流程架構 41 3.5.2 網格單元與平面網格尺寸規格 44 3.5.3 網格變形過程 46 3.6 有限元素模擬-Ansys Workbench 47 3.6.1 Ansys Workbench 簡介 47 3.6.2 模擬類型與材料參數設置 47 3.6.3 元素種類 48 3.6.4 Ansys Workbench API 49 3.7 Rhinoceros 3D 51 第四章 結果與討論 52 4.1 週次軟化對蒲松比影響之量測 52 4.1.1 DIC應變量測 52 4.1.2 ROC修正探討 53 4.1.3 準靜態之探討 57 4.1.4 工程應變與真實應變下之蒲松比 58 4.1.5 週次應力軟化下蒲松比變化 59 4.2 4D列印之平面網格形變 61 4.2.1 單元預應力與4D列印參數關係 61 4.2.2 模擬之預應力與熱膨脹比較 64 4.2.3 人為設計 67 4.2.4 變形受熱過程比較 74 4.3 深度學習之人臉網格資料庫生成 76 4.3.1 參數化設計-眼睛與眉毛 77 4.3.2 參數化設計-鼻子 80 4.3.3 參數化設計-嘴巴與人中 82 4.3.4 參數化設計-輪廓與全域曲率 84 4.3.5 參數化設計-案例分析 86 4.3.6 資料庫生成 88 4.3.7 資料後處理與3D人臉模型表示 89 4.4 深度學習反向設計 94 4.4.1 模型架構 94 4.4.2 結果與驗證探討 97 4.4.3 生成資料表式方式探討 107 4.5 人臉立體網格與立體曲面 109 第五章 結論與未來展望 111 5.1 結論 111 5.1.1 3D列印之超彈性材料蒲松比精密量測 111 5.1.2 4D列印之平面網格變形──面具製作搭配反向設計 112 5.2 未來展望 114 參考文獻 115 著作目錄 128 附錄一 期刊論文 129 " | |
| dc.language.iso | zh-TW | |
| dc.subject | 形狀記憶聚合物 | zh_TW |
| dc.subject | 週次軟化 | zh_TW |
| dc.subject | 熱塑性聚氨酯(TPU) | zh_TW |
| dc.subject | 平面影像關聯法 | zh_TW |
| dc.subject | 蒲松比 | zh_TW |
| dc.subject | 4D列印 | zh_TW |
| dc.subject | 形狀變形 | zh_TW |
| dc.subject | 反向設計 | zh_TW |
| dc.subject | 生成對抗網路 | zh_TW |
| dc.subject | 4D printing | en |
| dc.subject | Cyclic softening | en |
| dc.subject | Thermoplastic polyurethane (TPU) | en |
| dc.subject | Poisson’s ratio | en |
| dc.subject | Shape morphing | en |
| dc.subject | Shape memory polymer | en |
| dc.subject | Generative adversarial networks (GAN) | en |
| dc.subject | Inverse design | en |
| dc.subject | 2D-DIC | en |
| dc.title | 3D列印之超彈性材料蒲松比精密量測與4D列印之平面網格變形──面具製作搭配反向設計 | zh_TW |
| dc.title | Precise Measurement of Poisson’s Ratio of 3D-printed Hyperelastic Materials and Transformation of 2D grids into 3D gridshells by 4D Printing: A Case Study of Face Mask Design | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡佳霖(Hsin-Tsai Liu),李明蒼(Chih-Yang Tseng),劉建豪,王建凱 | |
| dc.subject.keyword | 週次軟化,熱塑性聚氨酯(TPU),平面影像關聯法,蒲松比,4D列印,形狀變形,反向設計,生成對抗網路,形狀記憶聚合物, | zh_TW |
| dc.subject.keyword | Cyclic softening,Thermoplastic polyurethane (TPU),2D-DIC,Poisson’s ratio,4D printing,Shape morphing,Inverse design,Generative adversarial networks (GAN),Shape memory polymer, | en |
| dc.relation.page | 129 | |
| dc.identifier.doi | 10.6342/NTU202101683 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-23 | |
| 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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