本文主要以本實驗室原有之光固化3D列印機為基礎，分別在軟硬體部分對其進行校準與性能改善，以得到更好之加工性能與列印穩定度。硬體部分本文先對原有3D列印機進行分析與機構改良，再藉由調整列印製程提升其列印性能，同時也提出一套新的雷射光路校準流程，依據流程進行校準即可確保雷射筆直且穩定的進行固化。軟體部分本文以原有實時觀測系統之觀測影像為基礎，透過深度學習與點特徵提取等影像處理方法對觀測影像進行分析，進而開發出基於影像之自動化校準系統，藉由此系統能夠省去人工調整列印參數所花費的時間與精力，同時擁有穩定之列印性能。最後再對原有之立體式微型撓性夾爪進行改良與分析，並透過校準與改良後之3D列印機進行製造，製造之成品將在本實驗室之微組裝系統進行實際應用測試。

This research is focused on the improvement of original stereolithography 3D printer. The software and hardware parts are calibrated separately to obtain better processing performance and printing stability. In the hardware part, this research will analyze and improve the mechanism of the original 3D printer, and then adjust the printing process to improve its printing performance. At the same time, a new laser optical path calibration process is proposed, which can ensure that the laser optical path is straight and the laser beam focus is stable. In the software part, this research is based on the observation images of the real-time observation system, and analyzes the images through image processing methods such as deep learning and point feature extraction, and then develops an image-based automatic calibration system. Through this system, time spent manually adjusting printing parameters can be saved while maintaining stable printing performance.

[1] 張栩毓，「擴增公設設計於微米級光固化3D 列印機之創新與升級」，國立成功大學機械工程所碩士論文，2018。
[2] 李建德，「微米級光固化3D列印機之實時製程觀測、分析與性能改善」，國立成功大學機械工程所碩士論文，2019。
[3] F. Willème, "Photo-sculpture," United States Patent 43822, 1864.
[4] J. E. Blanther, "Manufacture of contour relief maps," United States Patent 473901, 1892.
[5] H. Kodama, "Automatic method for fabricating a three‐dimensional plastic model with photo‐hardening polymer," Review of scientific instruments, vol. 52, no. 11, pp. 1770-1773, 1981.
[6] C. W. Hull, "Apparatus for production of three-dimensional objects by stereolithography," United States Patent 4575330, 1984.
[7] S. S. Crump, "Apparatus and method for creating three-dimensional objects," United States Patent 5121329, 1992.
[8] C. R. Deckard, J. J. Beaman, and J. F. Darrah, "Method for selective laser sintering with layerwise cross-scanning," United States Patent 5155324, 1992.
[9] Z. Weng, Y. Zhou, W. Lin, T. Senthil, and L. Wu, "Structure-property relationship of nano enhanced stereolithography resin for desktop SLA 3D printer," Composites Part A: Applied Science and Manufacturing, vol. 88, pp. 234-242, 2016.
[10] B. T. Phillips et al., "Additive manufacturing aboard a moving vessel at sea using passively stabilized stereolithography (SLA) 3D printing," Additive Manufacturing, vol. 31, p. 100969, 2020.
[11] 天馬科技，台灣，「光固化技術SLA及DLP比較 」[Online]. Available：https://taiwanteama.winshop.tw/pdf/2167, Accessed on：Dec. 2020.
[12] U. Delli and S. Chang, "Automated process monitoring in 3D printing using supervised machine learning," Procedia Manufacturing, vol. 26, pp. 865-870, 2018.
[13] X. Li, S. Siahpour, J. Lee, Y. Wang, and J. Shi, "Deep learning-based intelligent process monitoring of directed energy deposition in additive manufacturing with thermal images," Procedia Manufacturing, vol. 48, pp. 643-649, 2020.
[14] T. Hafkamp, G. van Baars, B. de Jager, and P. Etman, "Real-time feedback controlled conversion in vat photopolymerization of ceramics: A proof of principle," Additive Manufacturing, vol. 30, 2019.
[15] X. Zhao and D. W. Rosen, "Experimental validation and characterization of a real-time metrology system for photopolymerization-based stereolithographic additive manufacturing process," The International Journal of Advanced Manufacturing Technology, vol. 91, no. 1-4, pp. 1255-1273, 2017.
[16] X. Zhao and D. W. Rosen, "An implementation of real-time feedback control of cured part height in Exposure Controlled Projection Lithography with in-situ interferometric measurement feedback," Additive Manufacturing, vol. 23, pp. 253-263, 2018.
[17] C. H. J. Wells, Introduction to Molecular Photochemistry. London: Chapman and Hall, 1972.
[18] D. W. Rosen, "Stereolithography and rapid prototyping," in BioNanoFluidic MEMS: Springer, pp. 175-196, 2008.
[19] P. F. Jacobs, "Fundamentals of stereolithography," in 1992 International Solid Freeform Fabrication Symposium, 1992.
[20] F. Zernike, "Phase contrast," Ζ. Tech. Physik., vol. 16, p. 454, 1935.
[21] Y. Bengio, Learning deep architectures for AI. Now Publishers Inc, 2009.
[22] H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in Proceedings of the 26th annual international conference on machine learning, pp. 609-616, 2009.
[23] Y. LeCun et al., "Backpropagation applied to handwritten zip code recognition," Neural computation, vol. 1, no. 4, pp. 541-551, 1989.
[24] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[25] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, pp. 1097-1105, 2012.
[26] C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
[27] J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440, 2015.
[28] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention, pp. 234-241, 2015.
[29] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
[30] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
[31] J. M. Paros, L. Weisbord, "How to design flexure hinges," Mach. Des., pp. 151-156, 1965.
[32] 馮瑨，「微作業端效器之設計製造與測試」，國立成功大學機械工程所碩士論文，2001。
[33] R.J. Chang and Y.H. Lai, "Design and implementation of micromechatronic systems: SMA drive polymer microgripper," Design, Control and Applications of Mechatronic Systems in Engineering; IntechOpen: London, UK, p. 65, 2017.