積層製造技術是一種可快速製作具複雜外型產品的技術。在眾多積層製造技術中，熔融沉積成型(Fused Deposition Modeling, FDM)為較常見的方法之一。一直以來，FDM技術有兩個主要缺點：表面品質不佳與製造效率低落。影響FDM技術之製造效率與表面品質的因素主要有三：層與層堆疊方向、切層方式以及路徑生成與規劃，其中路徑之生成是更是FDM技術的基礎。一個完整沉積路徑包含有：垂直殼、水平殼、內部填充以及外部支撐等。而本研究將使用影像處理方式，針對各種基本沉積路徑，提出有效提升製造效率之路徑規劃。
　　另外，本研究考慮到客製化且一次性的應用之成品，使用完畢即丟棄，在強度需求較低的情況下，使用傳統的內部填充方式：以特定圖形進行模型內部等比例的填充，既耗時又不符合效益，並非是最理想的設計。倘若要以最短時間製造FDM模型，可將內部填充比例設定為0%，使其成為一中空物件。但由於移除內部填充路徑後，部份的沉積路徑下無足夠的支撐附著點，使模形表面產生孔洞。為達到兼顧效率與品質，本研究捨棄傳統內部填充方式，提出兩種內部支撐結構：柱狀結構內部支撐、分支結構內部支撐。此內部支撐僅在懸空特徵使用內部支撐結構，使模型懸空特徵下擁有足夠的附著點，讓材料可以成功的逐漸向上疊加，形成一個完整的實體模型。
　　本研究以五個範例來驗證本研究所發展的路徑規劃方式與內部支撐路徑生成演算法。在維持相同的表面品質下，結果發現與傳統內部填充方式相比，本研究之內部填充製造時間可減少約6%至16%；柱狀結構內部支撐製造時間可減少約19%至36%；分支結構內部支撐製造時間可減少約34%至54%，證明本方法可有效提高FDM技術的製造效率。

　　Additive manufacturing is a technology that can fabricate products with complex shape rapidly. Fused Deposition Modeling (FDM) is one of the commonly used manner in additive manufacturing field. However, FDM has two major disadvantages: poor surface quality and low manufacturing efficiency. Three factors that may influence these disadvantages: build orientation, slicing methods, and path generation and planning. Among these factors, the path generation is the key of FDM. The FDM paths are included vertical shell, horizontal shell, infill and support. This study will purpose a path planning method which can improve manufacturing efficiency via image processing, including all FDM paths.
　　In addition, this study takes into account the customized and one-time application which is discarded after use. In the case of lower intensity requirements, the use of traditional infll method, a specific pattern with specific infill ratio, is time-consuming and not in the best interest. It is not the best design. In order to get a FDM model as fast as possible, setting the infill ratio to 0% to make a hollow model is a triable strategy. However, due to the removal of the infill path, some of the deposition path may lack support structure and produce cavities on the surface of FDM model. In order to consider both manufacturing efficiency and model quality simultaneously, this study abandoned the traditional infill method and develops two path generation algorithm of inner support for supporting the near dangling features, including pillar-type inner support and branch-type inner support. With inner support structures, there are enough support points to sustain the deposition path on the top, so that the material can successfully stack from bottom to up, layer by layer, and generate a complete solid model.
　　This study will demonstrate the concept of the path planning and inner support by five cases to verify the result. Comparing with traditional infill method, the result shows that our proposed path planning method improves the 6-16% manufacturing efficiency, pillar-type inner support method improves the 19-36% manufacturing efficiency and branch-type inner support improves the 34-54% manufacturing efficiency. It is proved that this method can effectively improve the manufacturing efficiency of FDM technology.

[1] J. P. Kruth, M. C. Leu and T. Nakagawa, “Progress in Additive Manufacturing and Rapid
Prototyping”, CIRP Annals-Manufacturing Technology, Vol. 47, No. 2, pp. 525-540, 1998.
[2] T. Huang, S. Wang, K. He, “Quality Control for Fused Deposition Modeling based
Additive Manufacturing: Current Research and Future Trends”, The First International
Conference on Reliability Systems Engineering(ICRSE), pp. 1-6, Beijing, 2015.
[3] W. Han, M. A. Jafari, K. Seyed, “Process Speeding Up via Deposition Planning in Fused
Deposition-Based Layered Manufacturing Processes”, Rapid Prototyping Journal, Vol. 9,
No. 4, pp. 212-218, 2003.
[4] N. Siraskar, R. Paul, S. Anand, “Adaptive Slicing in Additive Manufacturing Process
using a Modified Boundary Octree Data Structure”, Journal of Manufacturing Science and
Engineering, Vol. 137, No. 1, 011007, 2015.
[5] Y. Jin, Y. He, G. Xue, J. Fu, “A Parallel-Based Path Generation Method for Fused
Deposition Modeling”, International Journal of Advanced Manufacturing Technology,
Vol. 77, No. 5-8, pp. 927-937, 2015.
[6] M. P. Zwier, W. W. Wits, “Design for Additive Manufacturing: Automated Build
Orientation Selection and Optimization”, Procedia CIRP, Vol. 55, pp. 128-133, 2016.
[7] K. Chalasani, L. Jones, L. Roscoe, “Support Generation for Fused Deposition Modeling”,
Proceedings of Solid Freeform Fabrication Symposium, pp. 229-241, University of Texas,
Austin, 1995.
[8] W. Han, M. A. Jafari, S. C. Danforth, A. Safari, “Tool Path-Based Deposition Planning in
Fused Deposition Processes”, Journal of Manufacturing Science and Engineering, Vol.
124, No. 2, pp. 462-472, 2002.
[9] Slic3r 官方網站，取自http://slic3r.org。
[10] P. Kulkarni, D. Dutta, “Deposition Strategies and Resulting Part Stiffnesses in Fused
Deposition Modeling”, Journal of Manufacturing Science and Engineering, Vol. 121, No.
1, pp. 93-103, 1999.
[11] R. V. Weeren, M. Agarwala, V. R. Jamalabad, A. Bandyophadyay, R.Vaidyanathan, N.
Langrana, A. Safari, P. Whalen, S. Danforth, C. Ballard, “Quality of Parts Processed by
Fused Deposition”, Proceedings of the Solid Freeform Fabrication Symposium, Vol. 6,
pp. 314-321, University of Texas, Austin, 1995.
106
[12] S. C. Park, B. K. Choi, “Tool-Path Planning for Direction-Parallel Area Milling”,
Computer-Aided Design, Vol. 32, No. 1, pp. 17-25, 2000.
[13] B. H. Kim, B. K. Choi, “Machining Efficiency Comparison Direction-Parallel Tool Path
with Contour-Parallel Tool Path”, Computer-Aided Design, Vol. 34, No. 2, pp. 89-95,
2002.
[14] Y. Jin, Y. He, J. Fu, W. Gan, Z. Lin, “Optimization of Tool-Path Generation for Material
Extrusion-Based Additive Manufacturing Technology”, Additive Manufacturing, Vol. 1,
pp. 32-47, 2014.
[15] M. K. Agarwala, V. R. Jamalabad, N. A. Langrana, A. Safari, P. J. Whalen, S. C. Danforth,
“Structural Quality of Parts Processed by Fused Deposition”, Rapid Prototyping Journal,
Vol. 2, No. 4, pp. 4-19, 1996.
[16] A. K. Sood, R. K. Ohdar, S. S. Mahapatra, “Parametric Appraisal of Mechanical Property
of Fused Deposition Modelling Processed Parts”, Materials & Design, Vol. 31, No. 1, pp.
287-295, 2010.
[17] P. Kulkarni, A. Marsan, D. Dutta, “A Review of Process Planning Techniques in Layered
Manufacturing”, Rapid Prototyping Journal, Vol. 6, No. 1, pp. 18-35, 2000.
[18] H. E. Otto, F. Kimura, F. Mandorli, U. Cugini, “Extension of Feature-Based CAD Systems
using TAE Structures to Support Integrated Rapid Prototyping”, Proceedings of the
Computers in Engineering Conference and the Engineering Data-base Symposium, pp.
779-793, 1995.
[19] Y. Jin, Y. He, J. Fu, “Support Generation for Additive Manufacturing based on Sliced
Data”, The International Journal of Advanced Manufacturing Technology, Vol. 80, No. 9-
12, pp. 2041-2052, 2015.
[20] A. Marsan, S. Allen, P. Kulkarni, D. Dutta, “An Integrated Software System for Process
Planning for Layered Manufacturing”, Proceedings of the Solid Freeform Fabrication
Symposium, Austin,, pp. 661-668, 1997.
[21] Q. Bo, Z. Lichao, S. Yusheng, L. Guocheng, “Support Fast Generation Algorithm based on
Discrete-Marking in Stereolithgraphy Rapid Prototyping”, Rapid Prototyping Journal,
Vol. 17, No. 6, pp. 451-457, 2011.
[22] X. Huang, C. Ye, S. Wu, K. Guo, J. Mo, “Sloping Wall Structure Support Generation for
Fused Deposition Modeling”, The International Journal of Advanced Manufacturing
Technology, Vol. 42, No. 11-12, pp. 1074-1081, 2009.
107
[23] X. Huang, C. Ye, J. Mo, H. Liu, “Slice Data Based Support Generation Algorithm for
Fused Deposition Modeling”, Tsinghua Science & Technology, Vol. 14, pp. 223-228,
2009.
[24] J. Vanek, J. A. G. Galicia, B. Benes, “Clever Support: Efficient Support Structure
Generation for Digital Fabrication”, Computer Graphics Forum, Vol. 33, No. 5, pp. 117-
125, 2014.
[25] P. Alexander, S. Allen, D. Dutta, “Part Orientation and Build Cost Determination in
Layered Manufacturing”, Computer-Aided Design, Vol. 30, No. 5, pp. 343-356, 1998.
[26] S. Allen, D. Dutta, “Wall Thickness Control in Layered Manufacturing for Surfaces with
Closed Slices”, Computational Geometry, Vol. 10, No. 4, pp. 223-238, 1998.
[27] C. L. Lim，快速成型原理與應用，郭啟全和鄭正元譯，高立，2004。
[28] 李鑫，邵茂官和張冰，「快速成型與製造技術發展現狀與趨勢」，北京化工大學技
術文章，2008。
[29] H. Takahashi, H. Miyashita, “Expressive Fused Deposition Modeling by Controlling
Extruder Height and Extrusion Amount”, Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems, pp. 5065-5074, 2017, New York, USA.
[30] Y. Jin, J. Du, Z. Ma, A. Liu, Y. He, “An Optimization Approach for Path Planning of
High-Quality and Uniform Additive Manufacturing”, The International Journal of
Advanced Manufacturing Technology, Vol. 92, No. 1-4, pp. 651-662, 2017.
[31] 鐘國亮，影像處理與電腦視覺，東華出版社，2015。
[32] 吳成柯等，數位影像處理，儒林圖書，1995。
[33] 熊郁昇，「應用於大型物體三維模型重建之多重二維校正板相機校正流程開發」，
國立中央大學，碩士論文，2016。
[34] R. C. Gonzalez, R. E. Woods, Digital Image Processing, 3rd Edition. Prentice Hall, New
York, 2007.
[35] 鄭文瑋，「在次像素精準度下的邊緣偵測演算法及其應用」，銘傳大學，碩士論文，
2005。
[36] 張宸銘，「應用視訊之自動化手勢軌跡追蹤系統」，中原大學，碩士論文，2009。
[37] 彭振軒，「使用樣板比對做進出口行人數量統計」，國立中央大學，碩士論文，2006。
[38] M. Nixon, A. Aguado, Feature Xxtraction and Image Processing, 2nd Edition. Academic
Press, UK, 2008.
108
[39] D. H. Douglas, T. K. Peucker, “Algorithms for the Reduction of the Number of Points
required to Represent a Digitized Line or its Caricature”, Cartographica: The International
Journal for Geographic Information and Geovisualization, Vol. 10, No. 2, pp. 112-122,
1973.
[40] G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV
Library, O’Reilly, 2008.
[41] 曾郁文，「雙光子光致聚合五軸微製造系統之雷射加工路徑生成研究」，國立中央
大學，碩士論文，2014。
[42] EmguCV 官方網站，取自http://www.emgu.com。
[43] OpenCV 官方網站，取自http://opencv.org。