|| 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.|
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 C. Yu and J. Jiang, "A perspective on using machine learning in 3D bioprinting," International Journal of Bioprinting, vol. 6, no. 1, 2020.
 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.
 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.
 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.
 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.
 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.
 D. X. Chen, "Extrusion bioprinting of scaffolds," in Extrusion Bioprinting of Scaffolds for Tissue Engineering Applications: Springer, 2019, pp. 117-145.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 S. J. Russell, Artificial intelligence a modern approach. Pearson Education, Inc., 2010.
 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.
 H. Sack. "Walter Pitts and the Mathematical Model of a Neural Network." http://scihi.org/walter-pitts-neural-network/
 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.
 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.
 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
 Amazon. "Amazon Machine Learning 開發人員指南." https://docs.aws.amazon.com/zh_tw/machine-learning/latest/dg/machinelearning-dg.pdf
 G. E. Box and K. B. Wilson, "On the experimental attainment of optimum conditions," in Breakthroughs in statistics: Springer, 1992, pp. 270-310.
 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.
 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
 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.
 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.
 Stat-Ease. "Design-Expert® software." www.statease.com
 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
 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.
 L. W. S. King. "Mini5-Axis-CNC." https://www.lihwoei.com.tw/mini5-axis-cnc-tw
 K. CORPORATION. "「粗糙度」入門." https://www.keyence.com.tw/ss/products/microscope/roughness/