生醫材料3D列印製程的建模與參數最佳化

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姓名

林建沅(Chien-Yuan Lin) 查詢紙本館藏

畢業系所

光機電工程研究所

論文名稱

生醫材料3D列印製程的建模與參數最佳化
(Modeling and Parameter Optimization of 3D Printing Process with Bio-material)

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摘要(中)

近年來穿戴型電子裝置逐漸普及於日常生活中，其中一種新型電子皮膚技術在該領域中陸續受到市場關注，如智慧型敷料可以透過監測傷口狀況達到釋放藥物以防止傷口感染或加速癒合的敷料。然而使用三維生物列印技術製作出符合傷口外型之客製化敷料，目前仍有許多缺點需要克服，以改善敷料對於慢性傷口的吻合度與包覆軟性陶瓷基板的平整度等。
本研究的主要目的是提升生醫材料所印製的產品表面品質。經實驗發現，產品形貌受到噴頭擠出壓力、噴頭與平台間隙和噴頭進給速度所影響。為了找出最佳製程參數，本研究提出實驗結合多層感知器與反應曲面法的方法，以實際應用案例驗證最佳化結果。第一階段使用單層單向單線的製造方式印製產品並量測形貌數值，建立品質預測模型。第二階段執行形貌列印最佳化的實際應用案例。最後，本研究以包含架構為3-9-6-3的多層感知器模型和雙因子交互模型，證明使用中央複合設計或全因子設計作為訓練資料集，均可取得預測誤差值為6.60 %以下的預測模型；以及證明使用實驗設計方法確實比隨機取用訓練資料集，更有效使用實驗數據且能減少生醫材料損耗。在模擬智慧光療感測型敷料的印製情境下，透過品質預測模型產生的最佳製程參數，控制產品形貌高度使印製表面平整化，驗證本研究提出的方法可以優化生醫材料所印製的產品表面品質。

摘要(英)

In recent years, wearable healthcare electronic devices are becoming popular in daily life, and a new electronic skin technology has received widespread attention in this field. As intelligent wound dressing can detect the injured condition and release drugs timely to prevent the wound from infection and accelerate wound healing. However, using 3D bioprinting technology to produce customized dressings that fit the wound profile, there still exists amounts of weak points. For example, the anastomosis of the dressing for chronic wounds and the flatness of the encapsulated soft ceramic substrate.
This study proposes a method to improve the surface quality of product printed with bio-material. The experiments show that the product shape is affected by three process parameters: the extrusion pressure of the material, the feed rate of the nozzle, and the gap between the nozzle and the platform. This study build models using Multilayer Perceptron and Response Surface methodology with the experimental data to optimize process parameters. Firstly, single-layer single-line samples are manufactured to collect the data from the product shape and the process parameters to train the models. Next, both of the Multilayer Perceptron model with 3-9-6-3 framework and the Two-Factor Interactions model, trained using the datasets of either Central Composite Design or Full Factorial Design, perform with the predict error of 6.60 % or less. This approach can reduce the consumption of bio-material compared to randomized datasets. Finally, in practical applications, the proposed method is verified to optimize the product surface quality printed with bio-material.

關鍵字(中)

★ 3D列印技術
★ 生醫材料
★ 多層感知器
★ 反應曲面法
★ 製程參數優化

關鍵字(英)

★ 3D Bioprinting
★ Biomaterials
★ Multilayer Perceptron
★ Response Surface Methodology
★ Process Optimization

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 IX
第一章緒論 1
1.1 研究背景 1
1.2 相關研究 3
1.2.1 擠出式生物列印 3
1.2.2 機器學習與3D列印製程優化 5
1.3 研究動機 7
1.4 論文架構 8
第二章相關理論 9
2.1 類神經網路 9
2.2 多層感知器 16
2.3 反應曲面法 17
2.4 實驗設計 20
第三章研究方法與執行步驟 24
3.1 研究方法 24
3.2 執行步驟 25
第四章實驗與建模 27
4.1 實驗設計 27
4.1.1 製程參數設計 27
A 中央複合設計實驗 29
B 全因子設計實驗 30
C 隨機實驗組合 32
4.1.2 產品生成與量化 34
A 3D生物列印實驗 35
B 產品形貌量測 37
C 產品形貌特徵 38
4.2 模型選用 39
4.2.1 反應曲面法 39
4.2.2 多層感知器 45
4.3 模型訓練結果與討論 48
4.3.1 RSM與MLP性能比較 49
4.3.2 CCD、FFD和隨機資料集的MLP模型性能比較 53
4.4 RSM實驗結果分析 54
第五章應用案例研究與討論 57
5.1 驗證模具設計與製造 57
5.2 最佳化製程參數 61
5.3 形貌列印最佳化 64
5.4 最佳化結果討論 69
第六章結論與未來展望 74
6.1 具體貢獻 74
6.2 未來展望 75
參考文獻 76

參考文獻

[1] Q. Pang et al., "Smart flexible electronics‐integrated wound dressing for real‐time monitoring and on‐demand treatment of infected wounds," Advanced Science, vol. 7, no. 6, p. 1902673, 2020.
[2] H. G. Yoo et al., "Flexible GaN LED on a polyimide substrate for display applications," in Quantum Sensing and Nanophotonic Devices IX, 2012, vol. 8268: SPIE, pp. 436-441.
[3] S. Y. Lee et al., "Water-resistant flexible GaN LED on a liquid crystal polymer substrate for implantable biomedical applications," Nano Energy, vol. 1, no. 1, pp. 145-151, 2012.
[4] E. S. Bishop et al., "3-D bioprinting technologies in tissue engineering and regenerative medicine: Current and future trends," Genes & diseases, vol. 4, no. 4, pp. 185-195, 2017.
[5] P. S. Gungor-Ozkerim, I. Inci, Y. S. Zhang, A. Khademhosseini, and M. R. Dokmeci, "Bioinks for 3D bioprinting: an overview," Biomaterials science, vol. 6, no. 5, pp. 915-946, 2018.
[6] A. Schwab, R. Levato, M. D’Este, S. Piluso, D. Eglin, and J. Malda, "Printability and shape fidelity of bioinks in 3D bioprinting," Chemical reviews, vol. 120, no. 19, pp. 11028-11055, 2020.
[7] S. Derakhshanfar, R. Mbeleck, K. Xu, X. Zhang, W. Zhong, and M. Xing, "3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances," Bioactive materials, vol. 3, no. 2, pp. 144-156, 2018.
[8] V. Mironov, V. Kasyanov, and R. R. Markwald, "Organ printing: from bioprinter to organ biofabrication line," Current opinion in biotechnology, vol. 22, no. 5, pp. 667-673, 2011.
[9] C. Yu and J. Jiang, "A perspective on using machine learning in 3D bioprinting," International Journal of Bioprinting, vol. 6, no. 1, 2020.
[10] A. Menon, B. Póczos, A. W. Feinberg, and N. R. Washburn, "Optimization of silicone 3D printing with hierarchical machine learning," 3D Printing and Additive Manufacturing, vol. 6, no. 4, pp. 181-189, 2019.
[11] M. Khanzadeh, P. Rao, R. Jafari-Marandi, B. K. Smith, M. A. Tschopp, and L. Bian, "Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts," Journal of Manufacturing Science and Engineering, vol. 140, no. 3, 2018.
[12] L. Scime and J. Beuth, "Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process," Additive Manufacturing, vol. 25, pp. 151-165, 2019.
[13] Z. Li, Z. Zhang, J. Shi, and D. Wu, "Prediction of surface roughness in extrusion-based additive manufacturing with machine learning," Robotics and Computer-Integrated Manufacturing, vol. 57, pp. 488-495, 2019.
[14] J. Shin, Y. Lee, Z. Li, J. Hu, S. S. Park, and K. Kim, "Optimized 3D Bioprinting Technology Based on Machine Learning: A Review of Recent Trends and Advances," Micromachines, vol. 13, no. 3, p. 363, 2022.
[15] D. X. Chen, "Extrusion bioprinting of scaffolds," in Extrusion Bioprinting of Scaffolds for Tissue Engineering Applications: Springer, 2019, pp. 117-145.
[16] X. Chen, M. Li, and H. Ke, "Modeling of the flow rate in the dispensing-based process for fabricating tissue scaffolds," Journal of manufacturing science and engineering, vol. 130, no. 2, 2008.
[17] M. Li, X. Tian, and X. Chen, "Modeling of flow rate, pore size, and porosity for the dispensing-based tissue scaffolds fabrication," Journal of manufacturing science and engineering, vol. 131, no. 3, 2009.
[18] M. Sarker and X. Chen, "Modeling the flow behavior and flow rate of medium viscosity alginate for scaffold fabrication with a three-dimensional bioplotter," Journal of Manufacturing Science and Engineering, vol. 139, no. 8, 2017.
[19] A. Malekpour and X. Chen, "Printability and cell viability in extrusion-based bioprinting from experimental, computational, and machine learning views," Journal of Functional Biomaterials, vol. 13, no. 2, p. 40, 2022.
[20] S. Naghieh, M. Sarker, N. Sharma, Z. Barhoumi, and X. Chen, "Printability of 3D printed hydrogel scaffolds: Influence of hydrogel composition and printing parameters," Applied Sciences, vol. 10, no. 1, p. 292, 2019.
[21] C. C. Chang, E. D. Boland, S. K. Williams, and J. B. Hoying, "Direct‐write bioprinting three‐dimensional biohybrid systems for future regenerative therapies," Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 98, no. 1, pp. 160-170, 2011.
[22] F. Caiazzo and A. Caggiano, "Laser direct metal deposition of 2024 Al alloy: trace geometry prediction via machine learning," Materials, vol. 11, no. 3, p. 444, 2018.
[23] M. Shirmohammadi, S. J. Goushchi, and P. M. Keshtiban, "Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm," Progress in Additive Manufacturing, vol. 6, no. 2, pp. 199-215, 2021.
[24] S. J. Russell, Artificial intelligence a modern approach. Pearson Education, Inc., 2010.
[25] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115-133, 1943.
[26] H. Sack. "Walter Pitts and the Mathematical Model of a Neural Network." http://scihi.org/walter-pitts-neural-network/
[27] J. Feng, X. He, Q. Teng, C. Ren, H. Chen, and Y. Li, "Reconstruction of porous media from extremely limited information using conditional generative adversarial networks," Physical Review E, vol. 100, no. 3, p. 033308, 2019.
[28] R. F. Turkson, F. Yan, M. K. A. Ali, and J. Hu, "Artificial neural network applications in the calibration of spark-ignition engines: An overview," Engineering science and technology, an international journal, vol. 19, no. 3, pp. 1346-1359, 2016.
[29] 黃鍾易, "將感測器信號和加工參數編碼成圖像用於雷射切割的遷移學習," 碩士, 機械工程學系, 國立中央大學, 桃園縣, 2021. https://hdl.handle.net/11296/vbgw5u
[30] Amazon. "Amazon Machine Learning 開發人員指南." https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/machinelearning-dg.pdf
[31] G. E. Box and K. B. Wilson, "On the experimental attainment of optimum conditions," in Breakthroughs in statistics: Springer, 1992, pp. 270-310.
[32] A. Witek-Krowiak, K. Chojnacka, D. Podstawczyk, A. Dawiec, and K. Pokomeda, "Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process," Bioresource technology, vol. 160, pp. 150-160, 2014.
[33] 劉虹每, "利用反應曲面法探討金針花一氧化氮清除活性成分最適化乙醇萃取條件," 碩士, 食品科學系, 東海大學, 台中市, 2013. https://hdl.handle.net/11296/5uv289
[34] R. H. Myers, D. C. Montgomery, and C. M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons, 2016.
[35] M. A. Bezerra, R. E. Santelli, E. P. Oliveira, L. S. Villar, and L. A. Escaleira, "Response surface methodology (RSM) as a tool for optimization in analytical chemistry," Talanta, vol. 76, no. 5, pp. 965-977, 2008.
[36] Stat-Ease. "Design-Expert® software." www.statease.com
[37] 洪承暉, "使用微型閥並具備自動平台校正功能之三維生物列印機開發," 碩士, 機械工程學系, 國立中央大學, 桃園縣, 2018. https://hdl.handle.net/11296/67hc96
[38] D. Kallweit, MEDILIGHT, U. RID, SignalGenerix, Microsemi, and Amires, "Flex LED Based Smart Light System for Healing of Chronic Wounds," LED Professional, 2019. https://www.led-professional.com/resources-1/articles/flex-led-based-smart-light-system-for-healing-of-chronic-wounds.
[39] L. W. S. King. "Mini5-Axis-CNC." https://www.lihwoei.com.tw/mini5-axis-cnc-tw
[40] K. CORPORATION. "「粗糙度」入門." https://www.keyence.com.tw/ss/products/microscope/roughness/

指導教授

黃衍任(Yean-ren Hwang)

審核日期

2022-8-16

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