快速原型制造中大尺寸模型的智能分割算法研究
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摘要
模型分割技术可解决待加工模型或零件因结构复杂、体积过大(超出材料尺寸或设备加工范围)等造成的整体加工困难、材料利用率低等问题,同时可以有效地降低加工设备要求、提高生产效率。针对巨型三维模型的快速原型制造而言,模型分割的目标不是单纯地把大模型分割成尺寸符合加工要求的小模型,而是要将大尺寸复杂模型分割成形状简单、尺寸适中、方便加工和装配的子模型,以提高快速成型设备的加工能力和效率。针对快速成形系统的通用数据接口--STL模型,存在大量数据冗余且没有拓
扑信息,无法直接进行表面曲率分析的问题,分析了三维模型的形体表达法与数据表示,使用Hash表算法对模型面片顶点进行了归并,研究了基于正向边结构的拓扑重构,构建了适用于边曲率分析的数据结构。
针对点曲率分析计算复杂,聚类和迭代运算量大的问题,研究了基于边曲率分析的特征边界提取算法。使用二面角、周长比和凸凹性三种曲率参数对模型表面进行曲率分析,提取信息直指可用于模型分割的特征边界。在此基础上,研究了基于遗传算法的特征边阀值选取,以最大类间方差作为适应度函数,该方法提取的特征边数目比预设阀值法减少了一半以上,且有效的特征边界都得以保留。
针对提取出的特征边在边链表中处于离散状态,必须链接起来才能用于模型分割的问题,研究了基于最优拟合平面的特征边链接算法。采用最小二乘法来生成特征边集的最优拟合平面,在拟合平面内合并孤立边,并使用Dijkstra最短路径法闭合特征边链。该方法可以有效闭合特征边界和去除噪声边,得到的特征环可以直接作为模型分割的边界。
针对用户有特殊分割要求或是必须进行手动分割的情况,研究了基于归纳学习法的交互式分割算法。在OpenGL环境下实现了用户对三维模型的交互式操作,由用户手工选取所要分割的位置(可以是点、边、面,或是划线选取),根据特征环和分割位置之间的位置和相似度关系建立决策树,以指导机器进行归纳学习,生成分割位置上的最优分割边界,以辅助用户完成模型分割。
针对模型自动分割中存在分割方案众多的问题,研究了基于多目标优化的智能化分割算法。该算法在保证子模型可被加工的前提下,综合考虑分割次数、子模型复杂度、材料使用率和分割面平整度等影响因素,建立了多目标优化函数,使用层次分割策略和多目标遗传算法来获取最优分割方案,以指导机器对模型进行自动分割。
针对现有模型分割研究中缺少有效的子模型装配结构生成算法,提出了一种相似形装配结构的自动生成算法。根据模型的切割轮廓和自身结构,基于Voronoi图的二等分法和切平面投影法,自动构建形状相似且尺寸适中的公/母装配结构。相似形装配结构的生成不需要单独建模,也无需计算合并位置,而是直接与子模型的闭合操作一并完成的。装配表面的三角化精度一致,且留有配合公差,有效地保证了子模型的装配精度。
基于以上模型分割和装配结构生成算法的研究,使用Visual C++编程软件,实现了快速原型制造的大尺寸模型分割和装配结构生成系统的开发。通过对大量实验模型进行的特征边界提取、交互式分割、智能化分割、以及相似形装配结构生成的实例,验证了模型分割和装配结构生成系统的有效性和通用性。
该论文有图87幅,表3个,参考文献170篇。
Model partitioning of rapid prototyping (RP) makes it possible to fabricate arbitrary large-scale models which are larger than the maximum dimension of the current RP machines. For the large-scale rapid prototype manufacturing, the objective of model partitioning is not just dividing a large model into a set of small sub-model, but decomposing the large complex model into several sub-models which are simple, moderate size and easy to fabricate and assembly.
Aiming at the data redundancy and no topology information of STL model that is the common data interface of RP system, the shape representation and data structure of STL file are analyzed. The Hash-table algorithm was used to merge the vertices of the model. The Forward edge structure was proposed to reconstruct the data topology of the model. The model denoising based on two-sided filter was used to improve the quality of the model surface.
Aiming at the complex clustering and iteration of the vertex-based curvature estimation, the edge-based curvature estimation was proposed to extract the feature edge. The calculation of the edge-based method is simpler than the vertex-based method. The three curvature parameters are dihedral angle, perimeter ration and convexity. The genetic algorithm (GA) was used to determinate the threshold of feature edge. The fitness function is the maximum variance of classes (MVC).
Aiming at the discreteness of feature edges in the edge chain, the best-fit plane (BFP) was proposed to group and link feature edges. The similar and adjacent feature edges were firstly grouped to be feature edge sets. The BFP of a feature edge set was calculated by using the least square method (LSM). The isolated feature edges can be grouped or deleted based on BFP. The Dijkstra’s algorithm was used to close the incomplete feature boundaries.
Aiming at the requirement of manual partitioning, the interactive partition method was researched. The interactive operation of STL model was achieved in the OpenGL environment. The partition positions (vertex, edge, facet, or scoring) can be selected by the operator using the mouse. The decision tree of cutting boundaries was constructed according to the relative position and similarity of the partition positions and the feature loops. Based on the inductive learing (IL), the optimal cutting boundaries were created on the partition positions for assisting the manual partitioning.
Aiming at the requirement of automatic partitioning, the intelligent partition method was researched. Generally, the large complex model has a lot of partition schemes. It is different to select the best scheme by the enumeration method. The multiobjective optimization (MOO) algorithm was proposed to select the best partition scheme. On condition that all sub-models should be smaller than the maximum dimension of the RP machine, cutting times, complexity of sub-models, utilization ratio of materials and planarity of cutting boundaries were synthetically considered. GA was also used to generate the optimal partition scheme for guiding the automatic partitioning.
Aiming at the effective construction of assembly features on the sub-models, the similar-shaped assembly feature was researched. According to the outside cutting contour, the similar-shaped contour was constructed by using the Voronoi diagram algorithm. The maximum assembly depth was calculated by using the projection method. Based on the similar-shaped contour and the maximum assembly depth, the similar-shaped assembly features were automatically created for facilitating the assembly of sub-models.
Based on the above research of model partitioning and assembly feature, a prototype system of model partition and assembly feature construction for large-scale rapid prototype manufacturing was developed by using Visual C++. More than ten experimental models were used to validate the validity and generality of the proposed algorithms and the developed system.
扑信息,无法直接进行表面曲率分析的问题,分析了三维模型的形体表达法与数据表示,使用Hash表算法对模型面片顶点进行了归并,研究了基于正向边结构的拓扑重构,构建了适用于边曲率分析的数据结构。
针对点曲率分析计算复杂,聚类和迭代运算量大的问题,研究了基于边曲率分析的特征边界提取算法。使用二面角、周长比和凸凹性三种曲率参数对模型表面进行曲率分析,提取信息直指可用于模型分割的特征边界。在此基础上,研究了基于遗传算法的特征边阀值选取,以最大类间方差作为适应度函数,该方法提取的特征边数目比预设阀值法减少了一半以上,且有效的特征边界都得以保留。
针对提取出的特征边在边链表中处于离散状态,必须链接起来才能用于模型分割的问题,研究了基于最优拟合平面的特征边链接算法。采用最小二乘法来生成特征边集的最优拟合平面,在拟合平面内合并孤立边,并使用Dijkstra最短路径法闭合特征边链。该方法可以有效闭合特征边界和去除噪声边,得到的特征环可以直接作为模型分割的边界。
针对用户有特殊分割要求或是必须进行手动分割的情况,研究了基于归纳学习法的交互式分割算法。在OpenGL环境下实现了用户对三维模型的交互式操作,由用户手工选取所要分割的位置(可以是点、边、面,或是划线选取),根据特征环和分割位置之间的位置和相似度关系建立决策树,以指导机器进行归纳学习,生成分割位置上的最优分割边界,以辅助用户完成模型分割。
针对模型自动分割中存在分割方案众多的问题,研究了基于多目标优化的智能化分割算法。该算法在保证子模型可被加工的前提下,综合考虑分割次数、子模型复杂度、材料使用率和分割面平整度等影响因素,建立了多目标优化函数,使用层次分割策略和多目标遗传算法来获取最优分割方案,以指导机器对模型进行自动分割。
针对现有模型分割研究中缺少有效的子模型装配结构生成算法,提出了一种相似形装配结构的自动生成算法。根据模型的切割轮廓和自身结构,基于Voronoi图的二等分法和切平面投影法,自动构建形状相似且尺寸适中的公/母装配结构。相似形装配结构的生成不需要单独建模,也无需计算合并位置,而是直接与子模型的闭合操作一并完成的。装配表面的三角化精度一致,且留有配合公差,有效地保证了子模型的装配精度。
基于以上模型分割和装配结构生成算法的研究,使用Visual C++编程软件,实现了快速原型制造的大尺寸模型分割和装配结构生成系统的开发。通过对大量实验模型进行的特征边界提取、交互式分割、智能化分割、以及相似形装配结构生成的实例,验证了模型分割和装配结构生成系统的有效性和通用性。
该论文有图87幅,表3个,参考文献170篇。
Model partitioning of rapid prototyping (RP) makes it possible to fabricate arbitrary large-scale models which are larger than the maximum dimension of the current RP machines. For the large-scale rapid prototype manufacturing, the objective of model partitioning is not just dividing a large model into a set of small sub-model, but decomposing the large complex model into several sub-models which are simple, moderate size and easy to fabricate and assembly.
Aiming at the data redundancy and no topology information of STL model that is the common data interface of RP system, the shape representation and data structure of STL file are analyzed. The Hash-table algorithm was used to merge the vertices of the model. The Forward edge structure was proposed to reconstruct the data topology of the model. The model denoising based on two-sided filter was used to improve the quality of the model surface.
Aiming at the complex clustering and iteration of the vertex-based curvature estimation, the edge-based curvature estimation was proposed to extract the feature edge. The calculation of the edge-based method is simpler than the vertex-based method. The three curvature parameters are dihedral angle, perimeter ration and convexity. The genetic algorithm (GA) was used to determinate the threshold of feature edge. The fitness function is the maximum variance of classes (MVC).
Aiming at the discreteness of feature edges in the edge chain, the best-fit plane (BFP) was proposed to group and link feature edges. The similar and adjacent feature edges were firstly grouped to be feature edge sets. The BFP of a feature edge set was calculated by using the least square method (LSM). The isolated feature edges can be grouped or deleted based on BFP. The Dijkstra’s algorithm was used to close the incomplete feature boundaries.
Aiming at the requirement of manual partitioning, the interactive partition method was researched. The interactive operation of STL model was achieved in the OpenGL environment. The partition positions (vertex, edge, facet, or scoring) can be selected by the operator using the mouse. The decision tree of cutting boundaries was constructed according to the relative position and similarity of the partition positions and the feature loops. Based on the inductive learing (IL), the optimal cutting boundaries were created on the partition positions for assisting the manual partitioning.
Aiming at the requirement of automatic partitioning, the intelligent partition method was researched. Generally, the large complex model has a lot of partition schemes. It is different to select the best scheme by the enumeration method. The multiobjective optimization (MOO) algorithm was proposed to select the best partition scheme. On condition that all sub-models should be smaller than the maximum dimension of the RP machine, cutting times, complexity of sub-models, utilization ratio of materials and planarity of cutting boundaries were synthetically considered. GA was also used to generate the optimal partition scheme for guiding the automatic partitioning.
Aiming at the effective construction of assembly features on the sub-models, the similar-shaped assembly feature was researched. According to the outside cutting contour, the similar-shaped contour was constructed by using the Voronoi diagram algorithm. The maximum assembly depth was calculated by using the projection method. Based on the similar-shaped contour and the maximum assembly depth, the similar-shaped assembly features were automatically created for facilitating the assembly of sub-models.
Based on the above research of model partitioning and assembly feature, a prototype system of model partition and assembly feature construction for large-scale rapid prototype manufacturing was developed by using Visual C++. More than ten experimental models were used to validate the validity and generality of the proposed algorithms and the developed system.
引文
[1]孙林岩.全球视角下的中国制造业发展[M].北京:清华大学出版社, 2008.
[2] http://www.gov.cn/gzdt/2010-06/11/content_1625361.htm
[3]焦振学.先进制造技术[M].北京:北京理工大学出版社, 1997.
[4]江苏省科技发展“十一五”规划纲要,苏政发(2006)52号, 2006.
[5]颜永年.先进制造技术[M].北京:化学工业出版社, 2002.
[6]于殿泓,李绦尘,卢秉恒.一种新型制造加工手段-快速成型制造技术[J].工具技术, 2000, 34(4): 8-10.
[7]朱林泉,白培康,朱江淼.快速成型与快速制造技术[M].北京:国防工业出版社, 2003.
[8]马黎,肖跃加,王从军.快速成型技术在新产品开发中的应用[J].机械设计, 1998(1):41-42.
[9] Jacobs, P. Rapid prototyping & manufacturing: fundamentals of stereo lithography [A]. 1st ed, SME, Dearborn, MI, 1992.
[10]郭九生,李彦生.基于激光快速成型技术的快速模具CAD[J].机械科学与设计, 1999, 18(1):158-159.
[11]云虹,张祥林.快速成型技术的应用[J].中国铸造装备与技术, 1996(5):46-48.
[12]崔国起,郭齐江.应用快速成型技术提高企业产品创新能力[J].科学学与科学技术管理, 1999, 20(8):53-55.
[13]孙琨,方亮,岑启宏,叶庆光,龚道明.新型聚苯乙烯泡沫塑料模三维快速制造技术[J].特种铸造及有色合金, 2006, 26(11):725-727.
[14] Sun, K., Li, B., Fang, L., and Ye, Q. A novel rapid prototyping system for expandable polystyrene [J]. Rapid Prototyping Journal, 2011, 17(1): 17-27.
[15] Thomas, C.L., Gaffney, T.M., Kaza, S., and Lee, C.H. Rapid prototyping of large scale aerospace structures [C]. Proceedings of Aerospace Applications Conference 1996, Vol. 4: 219-230.
[16] Zhu, J., Tanaka, R., Tanaka, T., and Saito, Y. An 8-axis robot based rough cutting system for surface sculpturing [C]. 11th International Conference on Precision Engineering (11th ICPE), Tokyo, Japan, 1996: 139-144.
[17] Ahn, D. G., Lee, S. H., and Yang D. Y. Development of transfer type variable lamination manufacturing (VLM-) process [J]. International Journal of Machine Tools andManufacture, 2002, 42(14): 1577-1587.
[18] Brooks, H. and Aitchison, D. A review of state-of-the-art large-sized foam cutting rapid prototyping and manufacturing technologies [J]. Rapid Prototyping Journal, 2010, 16(5): 318-327.
[19]法国CROMA公司.http://www.croma.fr
[20]美国Hotwire Direct公司.http://www.hotwiredirect.com
[21]波兰Demand Foam Cutting Systems公司.http://www.demandfoamcutting.com
[22]姜归荣.大型装备量化的开发与应用[J].机械设计与制造, 2002(3):109-110.
[23]孙晓鹏,李华.三维网格模型的分割及应用技术综述[J].计算机辅助设计与图形学学报, 2005, 17(8):1648-1655.
[24] Li, X., Toon, T., Tan, T., and Huang, Z. Decomposing polygon meshes for interactive applications [C]. Proceeding of the 2001 symposium on Interactive 3D Graphics: 35-42.
[25] Biederman, I. Recognition-by-component: A theory of human image understanding [J]. Psychological Review 1987, 94(2): 115-147.
[26]周晓蕊.三维建模软件的合理选配[J].设计新潮, 1999(1):39-40.
[27] Hoffman, D. D. and Richards, W. A. Parts of recognition [J]. Cognition, 1984, 18(1): 65-96.
[28] Chen, Y. and Medioni, G. Object Modeling by Registration of Multiple Range Images [J]. Image and Vision Computing, 1992, 10(3): 145-155.
[29] Cass, T. A. Robust Affine Structure Matching for 3D Object Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1265-1274.
[30] Sclaroff, S. and Pentland, A. P. Modal Matching for Correspondence and Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995,17(6): 545–561.
[31] Weiss, I. and Ray, M. Model-based recognition of 3D object from single vision [J]. IEEE Trans. PAMI, 2001, 23(2):116–128.
[32]王会凤,周雄辉,王婉.基于特征的模型多目标优化分割算法[J].机械工程学报, 2008(7):241-247.
[33]孙琨,岑启宏,孙蔚.带有装配定位系统的STL模型直接生成算法研究[J].机械制造, 2004, 42(3):24-26.
[34]徐兵.机械装配技术[M].北京:中国轻工业出版社, 2005.
[35] Dhaliwal, S. and Gupta, S. K. A feature-based approach to automated design of multi-piece sacrificial molds [J]. Journal of Computing and Information Science in Engineering, 2001, 1: 225–234.
[36]曾亚君,曾国华,曹申.多面体模型的新型空间结构快速建模方法研究[J].河北建筑科技学院学报, 2006, 23(3): 20-24.
[37] Su, Y., et al. Automatic mesh generation and modification techniques for mixed quadrilateral and hexahedral element meshes of non-manifold models [J], Computer-Aided Design 582 36 (2004) 581–594.
[38] Peng, Q. and Loftus, M. A New Approach to Reverse Engineering Based on Vision Information [J]. International Journal of Machine Tools & Manufacture, 1998, 38: 881-899.
[39] Chazelle, B., Dobkin, D. P., Shouraboura, N., et al. Strategies for polyhedral surface decomposition: An experimental study [J]. Computational Geometry: Theory and Application, 1997, 7(4/5): 327-342.
[40] Hoover, A., Jean-Baptiste, G., and Jiang, X. An experimental comparison of range image segmentation algorithms [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1996, 18(7): 673-689.
[41] Vincent, L. and Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
[42] Arora, S., Raghavan, P., and Rao, S. Approximation Schemes for Euclidean k-median and Related Problems [C]. 30th Ann. ACM Symp. Theory of Computing, 1998: 106-113.
[43] Capoyleas, V., Rote, G., and Woeginger, G. Geometric Clustering [J]. Journal of Algorithms, 1991, 12: 341-356.
[44] Shamir, A. Feature space analysis of unstructured meshes [A]. IEEE Visualization, Seattle, Washington, 2003: 185-192.
[45] Hebert, M., Homan, R., and Johnson, A., et al. Sensor-based interior modeling [A]. American Nuclear Society 6th Topical Meeting on Robotics and Remote Systems, Monterey, California, 1995: 731-737.
[46] Trucco, E. and Fisher, R. B. Experiments in curvature-based segmentation of range data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 177-182.
[47] Sagi, K. and Ayellet, T. Hierarchical mesh decomposition using fuzzy clustering and cuts [J]. ACM Transactions on Graphics, 2003, 22(3): 954-961.
[48] Bischoff, S. and Kobbelt, L. Ellipsoid decomposition of 3D-models [A]. International Symposium on 3D Data Processing, Visualization and Transmission, New York, 2002: 480-488.
[49] Li, X., Woon, T. W., Tan, T. S., et al. Decomposing polygon meshes for interactive applications [A]. ACM Symposium on Interactive 3D Graphics, Research Triangle Park, North Carolina, 2001: 35-42.
[50] Lazarus, F. and Verroust, A. Level set diagrams of polyhedral objects [A]. 5th Symposium on Solid Modeling and Applications, New York, 1999: 130-140.
[51] Paul, A. and Debarry. Watersheds: Processes, Assessment and Management [M]. John Wiley & Sons, 2004.
[52] Serra, J. P. Image Analysis and Mathematical Morphology [M]. Academic Press, 1982.
[53] Mangan, A. and Whitaker, R. Partitioning 3D Surface Meshes Using Watershed Segmentation [J]. IEEE Transactions on Visualization and Computer Graphics, 1999, 5(4): 308-321.
[54] Pulla, S. Curvature based segmentation for 3-Dimensional meshes [D]. Tempe, AZ: Arizona State University, 2001.
[55] Rettmann, M. E., Han, X., and Prince, J. L. Automated surface segmentation using Watersheds on the cortical surface [J]. Neuro Image, 2002, 15(2): 329-344.
[56] Marty, K. Segmentation of a human brain cortical surface mesh using Watersheds [R]. Madison: University of Wisconsin, CS766 Final Project, 2002.
[57] Page, D. L., Koschan, A. F., and Abidi, M. A. Perception-based 3D triangle mesh segmentation using fast marching Watersheds [A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, 2003: 27-32.
[58] Zhang, Y., Paik, J. K., Koschan, A., et al. A Simple and Efficient Algorithm for Part Decomposition of 3-d Triangulated Models Based on Curvature Analysis [A]. International Conference on Image Processing, Rochester, NY, USA, 2002: 273-276.
[59]廖毅,程志全,党岗.一种基于显著性分析的网格分割算法[C].第一届中国图学大会暨第十届华东六省一市工程图学学术年会, 2007.
[60] Garland, M., Willmott, A., and Heckbert, P. S. Hierarchical face clustering on polygonal surfaces [C]. 2001 Symposium on Interactive 3D Graphics, March 19-21, 2001, Research Triangle Park, NC, United states, 2001: 49-58.
[61] Shlafman, S., Tal, A., and Katz, S. Metamorphosis of polyhedral surfaces using decomposition [A]. Annual Conference of the European Association for Computer Graphics, Saarbrücken, 2002: 219–228.
[62] Kanungo, T., et al. An efficient k-means clustering algorithms: Analysis and implementation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 881-892.
[63] Katz, S. and Tal, A. Hierarchical mesh decomposition using fuzzy clustering and cuts [J]. Acm Transactions on Graphics, 2003, 22: 954-961.
[64]陈国栋,李建微,潘林,余轮.基于人体特征三维人体模型的骨架提取算法[J].计算机科学, 2009, 36(7): 295-297.
[65] Leymarie, F. and Kimia, B. The Shock Scaffold for Representing 3D Shape [A]. Proceedings of the 4th International Workshop on Visual Form, Capri, Italy, 2001: 216-228.
[66] Teichmann, M. and Teller, S. Assisted Articulation of Closed Polygonal Models [C]. Proc. 9th Eurographics Workshop on Animation and Simulation, 1998: 254-268.
[67] Lazarus, F. and Verroust, A. Level set diagrams of polyhedral objects [A]. Fifth Symposium on Solid Modeling and Applications, New York, NY, USA, 1999: 130-140.
[68] Verroust, A. and Lazarus F. Extracting Skeletal Curves from 3D Scattered Data [J]. The Visual Computer, 2000, 16(1): 15-25.
[69] Hisada, M., et al. A 3D Voronoi-based Skeleton and Associated Surface Feature [C]. Pacific Conference on Computer Graphics and Applications, 2001: 89-96.
[70] Hilaga, M., Shinagawa, Y., Komura, T. and Kunni. T. Topology matching for fully automatic similarity estimation of 3D shapes [A]. Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH, Los Angeles, California, 2001: 203-212.
[71] Sebastian, T. B., Klein, P. N., Kimia. B. B. Recognition of Shapes by Editing Their Shock Graphs [J]. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26(5): 550-571.
[72] Oda, T., Itoh, Y., Nakai, W., et a1. Interactive Skeleton Extraction using Geodesic Distance [C]. Artificial Reality and Telex stances Workshops, 2006.ICAT’06, 16th International Conference, Nov 2006: 275-281.
[73] Li, X., et al. Decomposing polygon meshes for interactive applications [C]. 2001 Symposium on Interactive 3D Graphics, Research Triangle Park, NC, United states, 2001: 35-42.
[74]孙晓鹏.三维模型的分割与应用研究[D].中国科学院计算技术研究所, 2005.
[75] Shewchuk, J. R. Delaunay refinement algorithms for triangular mesh generation [J]. Computational Geometry-Theory and Applications, 2002, 22: 21-74.
[76]陈圆,王从军,黄树槐.基于STL文件的阶梯剖分算法研究[J].华中科技大学学报, 2004, 32(6): 19-21.
[77] Chan, C. K. and Tan, S. T. Generating assembly features onto split solid models [J]. Computer Aided Design, 2003, 35: 1315-1336.
[78] Delebecque, B., Houtmann, Y., Lauvaux, G., and Barlier, C. Automated generation of assembly features in layered manufacturing [J]. Rapid Prototyping Journal, 2008, 14(4): 234-245.
[79]莫健华.快速成形与快速制模[M].北京:电子工业出版社, 2006.
[80]郭丽华,王从军,黄树槐. CAD文件的STL格式转换[J].应用RP技术的市场商机和解决方案研讨会论文集,武汉,华中科技大学, 2000: 87-92.
[81]徐人平.快速原型技术与快速设计开发[M].北京:化学工业出版社, 2008.
[82] Schaub, D. A. Optimizing Stereolithograph Thought [J]. Journal of Manufacturing Systems, 1997, 16: 290-303.
[83] http://www.autodesk.com/techpubs/autocad/dxf/reference/
[84] Initial Graphics Exchange Specification (IGES) Version 3.0 (U. S.) National Bureau of Standard (NEL) Gaithersburg, ND. PB 86-199757, Apr. 86.
[85] Altemueller J. The STEP file structure. Doc. No.4. 2. 1, Version 12.0 London: ISO TC184/SC4/WG1, 1998: 11-38.
[86] 3D System Inc. Stereolithgraphy Interface Specification [R], July, 1988.
[87]杜忠于,尹希猛,王运赣,黄树槐.快速光造型技术中数据模型的诊断[J].计算机工程, 1995, 21(4): 3-5.
[88]田宗军,李小林,黄因慧.快速成形系统中STL文件的缺陷与修复[J].电加工, 1999, 02: 15-18.
[89]李江峰,钟约先,李电生.一种基于边界缺陷的STL文件修补算法[J].计算机应用, 2002, 31(3): 45-47.
[90]张剑峰,黄因慧,赵剑峰.快速成型系统中STL模型缺陷的自动修复技术[J].扬州大学学报(自然科学版), 2002, 5(2): 33-36.
[91]陈立亮,刘瑞祥,林汉同.基于STL网格剖分技术的研究[J].特种铸造及有色合金, 1999, 1: 145-147.
[92]王丽丽.基于邻域关系的STL文件拓扑重建技术[J].机械制造与研究, 2008, 37(3): 24-29.
[93] Lipman, Y. and Sorkine, O. Linear rotation-invariant coordinates for meshes [J]. ACM Transaction on Graphics, 2005, 24(3): 479-487.
[94] Hradek, J., Kuchai, M., and Skala, V. Hash functions and triangular mesh reconstruction [J]. Computer & Geosciences, 2003, 29: 741-751.
[95] Baumgart, B. G. A polyhedron representation for computer vision[A].National Computer Conference, Anaheim, CA, 1975: 589-596.
[96] Guibas, L. and Stofi, J. Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams [J]. ACM Transactions on Graphics, 1985, 4(3): 74-123.
[97]戴宁,廖文和,陈春美.STL数据快速拓扑重建关键算法[J].计算机辅助设计与图形学学报, 2005, 11(17): 2447-2452.
[98]刘之生,黄纯颖.反求工程技术[M].北京:机械工业出版社, 1992.
[99] Mencl, R. and Muller, H. Interpolation and approximation of surface from threedimensional scattered data points [R]. State of the Art Reports, Eurographics, 1998: 51-67.
[100]张舜德,朱东波,卢秉恒.反求工程中三维几何形状测量及数据预处理[J].机电工程技术, 2001, 30(l): 7-10.
[101]刘立国.点云模型的光顺去噪研究[D].浙江大学硕士学位论文, 2007.
[102] Tubin, G. A signal Processing approach to fair surface design [C]. Proc. of the Computer Graphics. Annual Conf. Series, ACM SIGGRAPH, 1995: 351-358.
[103] Desbrun, M., Meyer, M., Sehroder, P., et al. Implicit fairing of irregular meshes using diffusion and curvature flow [C]. Proc. of the Computer Graphics. Annual Conf. Series, ACM SIGGRAPH, 1999: 317-324.
[104] Perona, P. and Malik, J. Scale-Space and edge detection using anisotropic diffusion [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
[105] Clarenz, U., Diewald, U., and Rumpf, M. Anisotropic geometric diffusion in surface processing [C]. Proc. of the IEEE Visualization, 2000: 397-405.
[106] Tomasi, C. and Manduchi, R. Bilateral filtering for gray and color images [C]. Proc. Of the 6th International Conference on Computer Vision, 1998: 839-846.
[107] Jones, T., Durand, F., and Desbrun, M. Noniterative, feature preserving mesh smoothing [C]. Proc. Of SIGGRAPH03, San Diego, 2003: 943-949.
[108] Fleishman, S., Drori, I., and Cohen-Or, D. Bilateral mesh denoising [C]. Proc. of ACM SIGGRAPH, 2003: 950-953.
[109] Porteous, I. R. Geometric Differentiation [M]. Cambridge University Press, 1994.
[110]徐天长.关于曲面的高斯曲率的应用[J].安庆师范学院学报(自然科学版), 2007, 13(1): 30-31.
[111]欧阳江帆,宋晓冰,袁美玲,柳秀霞.基于均方根曲率和绝对曲率的三维人脸特征定位[J].福建电脑, 2009, 3: 70-71.
[112] Gatzke, T., Grimm, C., Garland, M., and Zelinka, S. Curvature maps for local shape comparison [C]. Shape Modeling and Applications, 2005 International Conference, 13-17 June 2005: 244-253.
[113] Huang, J. and Menq, C. H. Combinatorial manifold mesh reconstruction and optimization from unorganized points with arbitrary topology [J]. Computer-Aided Design, 2002, 34: 149-165.
[114]华刚,郑南宁,薛建儒.基于改进遗传算法的边缘检测阈值自动选取及其应用[J].小型微型计算机系统, 2002, 23(3):318-321.
[115] Luger, G. F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Fifth Edition) [M]. MA, Addison-Wesley, 2004.
[116]王小平.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社, 2002.
[117] Goldberg, D. E. Genetic Algorithm in Search, Optimization, and Machine Learning [M]. MA, Addison-Wesley, 1989.
[118] Yang, S., Shu, S., and Zhu, S. Feature line extraction from triangular meshes based on STL files [J]. Computer Engineering and Application, 2008, 44(4): 14-19.
[119] Bajaj, C. L. and Dey, T. K. Convex decomposition of polyhedral and robustness [J]. Siam Journal on Computing, 1992, 21(2): 339-364.
[120] Fitzgibbon, A., Pilu, M., and Fisher, R. B. Direct least square fitting of ellipses [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 476-480.
[121] Chen, J. and Han, Y. Shortest path on the polyhedron [C]. Proceedings of the 6th ACM Symposium on Computer Geometry, Berkley, California, 1990: 360-369.
[122] Kanai, T. and Suzuki, H. Approximate shortest path on a polyhedral surface and its applications [J]. Computer-Aided Design, 2001, 33(11): 801-811.
[123] Foley, J. D., van Dam, A., Feiner, S. K., and Hughes, J. F. Computer graphics, Second Edition in C [M]. USA: Pearson Education, 1996.
[124] Zhang, BX and Mouftah, HT. A destination-driven shortest Path tree algorithm [D]. IEEE Communications Society, ed. Proc. of the 2002 IEEE Inter. Conf. on Communications, 2002, 4: 2258-2262.
[125] Langley, P. Elements of Machine Learning [M]. Morgan Kaufmann, 1995.
[126]周志华,王钰.机器学习及其应用2009[M].北京:清华大学出版社, 2009.
[127]刑五丰,易健康.归纳学习方法在结构损伤识别中的比较研究[J].计算机与信息技术, 2006, Z1: 1-3.
[128]洪家荣.归纳学习——算法、理论、应用[M].北京:科学出版社, 1997.
[129] Safavian, S. R. and Landgrebe, D. A survey of decision tree classifier methodology [J]. IEEE Transactions on Systems, Man and Cybernetics, 1991, 21(3): 660-674.
[130] Furnkranz, J. Separate- and-Conquer Rule Learning [J]. Artificial Intelligence Review, 1999, 13: 3-54.
[131] Hothorn, T. and Lausen, B. Bagging tree classifiers for laser scanning images: a data-and simulation-based strategy [J]. Artificial Intelligence in Medicine, 2003, 27(1): 65-79.
[132] Chen, S., Cowan, C. F. N., and Grant, P. M. Orthogonal least squares learning algorithm for radial basis function networks [J]. IEEE Transactions on Neural Networks, 1991, 2(2): 302-309.
[133] Konar, A. Computational Intelligence: Principles, Techniques and Applications [M]. Berlin: Springer, 2004.
[134] Shreiner, D., et al. OpenGL Programming Guide, Fifth Edition: The Official Guide to Learning OpenGL [M].北京:机械工业出版社, 2006.
[135]乔林等.OpenGL程序设计[M].北京:清华大学出版社, 2000.
[136]陈太喜,方亮,刘艳伟,贾龙.基于OpenGL的STL数据模型建模方法[J].机床与液压, 2009, 37(3): 155-158.
[137]徐雪松.STL模型表面点快速拾取技术[J].工程图学学报, 2005, 26(3): 18-22.
[138] Stroud, I. and Xirouchakis, P. C. STL and extensions [J]. Advances in Engineering Software, 2000, 31(2): 83-95
[139] Quinlan J. R. Induction of Decision Trees [J]. Machine Learning, 1986, 1: 81-106.
[140] Hong, J. R. and Mao, C. J. IDAQ: A combination of ID3 and AQ15 [C]. Proceedings of the Second World Congress on Expert Systems, Lisbon, Portugal, 1994: 210-234.
[141]金聪,郭京蕾.人工智能原理与应用[M].北京:清华大学出版社, 2006.
[142]阳明盛,罗长童.最优化原理、方法及求解软件[M].北京:科学出版社, 2009.
[143]林锉云,董加礼.多目标优化的方法与理论[M].吉林:吉林教育出版社, 1992.
[144] Pareto, V. Course Economic Politique. Vol. I and II [A]. Lausanne: Rouge, 1896.
[145] Kuhn, H. W. and Tucker, A. W. Nonlinear Programming [C]. Proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1952.
[146] Garey, M. R., Johnson, D. S., and Sethi, R. The complexity of flowshop and jobshop scheduling [J]. Mathematics of Operations Research, 1976: 117-129.
[147]焦李成.多目标优化免疫算法、理论和应用[M].北京:科学出版社, 2010.
[148]郑金华.多目标进化算法及其应用[M] .北京:科学出版社, 2007.
[149] Steuer, R. E. Multiple Criteria Optimization: Theory, Computation, and Application [M]. New York: Wiley, 1986.
[150] Runarsson, T. P. and Yao. X. Stochastic ranking for constrained evolutionary optimization [J]. IEEE Transaction on Evolutionary Computation, 2000, 4(3):284-294.
[151] Zheng, D., Gen, M., and Cheng, R. Multiobjective optimization using genetic algorithms [J]. Engineering Valuation and Cost Analysis, 1999, 2: 303-310.
[152] Fonseca, C. M. and Fleming, P. J. Genetic algorithm for multiobjective optimization: Formulation, discussion and generation [C]. Proceedings of 5th International Conference on Genetic Algorithms. San Mateo: Morgan Kauffman Publishers, 1993: 416-423.
[153] Srinivas, N. and Deb, K. Multiobjective optimization using non-dominated sorting in genetic algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-248.
[154] Schaffer, J. D. Multiple objective optimization with vector evaluated genetic algorithms [C].Proceedings of 1st International Conference on Genetic Algorithms and Their Applications. Hillsdale: Lawrence Erlbaum Associates, Inc., 1985: 93-100.
[155] Zitzler, E. and Thiele, L. Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach [J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.
[156] Knowles, J. D. and Corne, D. W. Approximating the non-dominated front using the Pareto archived evolution strategy [J]. Evolutionary Computation, 2000, 8(2): 149-172.
[157] [日]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社, 2000.
[158] Coello, C. A., Pulido, G. T., and Lechuga, M. S. Handing multiple objectives with particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279.
[159]郭戈等.快速成形技术[M].北京:化学工业出版社, 2005.
[160]王泉德.任意三角网格模型体积的快速准确计算方法[J].计算机工程与应用, 2009, 45(18): 32-34.
[161]王会凤.复杂模型分割制造关键技术研究及应用[D].上海交通大学博士论文, 2008.
[162]龚奇伟,张李超,莫健华.STL模型自动镂空的算法与应用[J].计算机辅助设计与图形学学报, 2007, 19(1): 54-58.
[163] Holland, W. V. and Bronsvoort, W. F. Assembly features in modeling and planning [J]. Robot Computer Integrated Manufacturing, 2000, 16: 277–294.
[164] De Fazio, T. L. et al. A prototype of feature-based design for assembly [C]. ASME Advanced Design Automation, 1990: 9-16.
[165] Gopi, M., Krishnan, S., and Silva, C. T. Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation [J]. Computer Graphics Forum, 2000, 19(3): 467-478.
[166] Kim, D. S., D. Kim, et al. Voronoi diagram of a circle set from Voronoi diagram of a point set: II. Geometry [J]. Computer Aided Geometric Design, 2001, 18(6): 563-585.
[167] Held, M. Voronoi diagrams and offset curves of curvilinear polygons [J]. Computer-Aided Design, 1998, 30(4): 287-300.
[168] Mayya, N. and Rajan, V. T. An efficient shape-representation scheme using Voronoi skeletons [J]. Pattern Recognition Letter, 1995, 16(2): 147-160.
[169]孙琨,岑启宏,孙蔚.带有装配定位系统的STL模型直接生成算法研究[J].机械制造,2004,42(3): 24-26.
[170] Z-corp公司的Zprinter450成型机. http://www.zcorp.com/en/Products/3D-Printers/ZPrinter-450/spage.aspx
[2] http://www.gov.cn/gzdt/2010-06/11/content_1625361.htm
[3]焦振学.先进制造技术[M].北京:北京理工大学出版社, 1997.
[4]江苏省科技发展“十一五”规划纲要,苏政发(2006)52号, 2006.
[5]颜永年.先进制造技术[M].北京:化学工业出版社, 2002.
[6]于殿泓,李绦尘,卢秉恒.一种新型制造加工手段-快速成型制造技术[J].工具技术, 2000, 34(4): 8-10.
[7]朱林泉,白培康,朱江淼.快速成型与快速制造技术[M].北京:国防工业出版社, 2003.
[8]马黎,肖跃加,王从军.快速成型技术在新产品开发中的应用[J].机械设计, 1998(1):41-42.
[9] Jacobs, P. Rapid prototyping & manufacturing: fundamentals of stereo lithography [A]. 1st ed, SME, Dearborn, MI, 1992.
[10]郭九生,李彦生.基于激光快速成型技术的快速模具CAD[J].机械科学与设计, 1999, 18(1):158-159.
[11]云虹,张祥林.快速成型技术的应用[J].中国铸造装备与技术, 1996(5):46-48.
[12]崔国起,郭齐江.应用快速成型技术提高企业产品创新能力[J].科学学与科学技术管理, 1999, 20(8):53-55.
[13]孙琨,方亮,岑启宏,叶庆光,龚道明.新型聚苯乙烯泡沫塑料模三维快速制造技术[J].特种铸造及有色合金, 2006, 26(11):725-727.
[14] Sun, K., Li, B., Fang, L., and Ye, Q. A novel rapid prototyping system for expandable polystyrene [J]. Rapid Prototyping Journal, 2011, 17(1): 17-27.
[15] Thomas, C.L., Gaffney, T.M., Kaza, S., and Lee, C.H. Rapid prototyping of large scale aerospace structures [C]. Proceedings of Aerospace Applications Conference 1996, Vol. 4: 219-230.
[16] Zhu, J., Tanaka, R., Tanaka, T., and Saito, Y. An 8-axis robot based rough cutting system for surface sculpturing [C]. 11th International Conference on Precision Engineering (11th ICPE), Tokyo, Japan, 1996: 139-144.
[17] Ahn, D. G., Lee, S. H., and Yang D. Y. Development of transfer type variable lamination manufacturing (VLM-) process [J]. International Journal of Machine Tools andManufacture, 2002, 42(14): 1577-1587.
[18] Brooks, H. and Aitchison, D. A review of state-of-the-art large-sized foam cutting rapid prototyping and manufacturing technologies [J]. Rapid Prototyping Journal, 2010, 16(5): 318-327.
[19]法国CROMA公司.http://www.croma.fr
[20]美国Hotwire Direct公司.http://www.hotwiredirect.com
[21]波兰Demand Foam Cutting Systems公司.http://www.demandfoamcutting.com
[22]姜归荣.大型装备量化的开发与应用[J].机械设计与制造, 2002(3):109-110.
[23]孙晓鹏,李华.三维网格模型的分割及应用技术综述[J].计算机辅助设计与图形学学报, 2005, 17(8):1648-1655.
[24] Li, X., Toon, T., Tan, T., and Huang, Z. Decomposing polygon meshes for interactive applications [C]. Proceeding of the 2001 symposium on Interactive 3D Graphics: 35-42.
[25] Biederman, I. Recognition-by-component: A theory of human image understanding [J]. Psychological Review 1987, 94(2): 115-147.
[26]周晓蕊.三维建模软件的合理选配[J].设计新潮, 1999(1):39-40.
[27] Hoffman, D. D. and Richards, W. A. Parts of recognition [J]. Cognition, 1984, 18(1): 65-96.
[28] Chen, Y. and Medioni, G. Object Modeling by Registration of Multiple Range Images [J]. Image and Vision Computing, 1992, 10(3): 145-155.
[29] Cass, T. A. Robust Affine Structure Matching for 3D Object Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1265-1274.
[30] Sclaroff, S. and Pentland, A. P. Modal Matching for Correspondence and Recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995,17(6): 545–561.
[31] Weiss, I. and Ray, M. Model-based recognition of 3D object from single vision [J]. IEEE Trans. PAMI, 2001, 23(2):116–128.
[32]王会凤,周雄辉,王婉.基于特征的模型多目标优化分割算法[J].机械工程学报, 2008(7):241-247.
[33]孙琨,岑启宏,孙蔚.带有装配定位系统的STL模型直接生成算法研究[J].机械制造, 2004, 42(3):24-26.
[34]徐兵.机械装配技术[M].北京:中国轻工业出版社, 2005.
[35] Dhaliwal, S. and Gupta, S. K. A feature-based approach to automated design of multi-piece sacrificial molds [J]. Journal of Computing and Information Science in Engineering, 2001, 1: 225–234.
[36]曾亚君,曾国华,曹申.多面体模型的新型空间结构快速建模方法研究[J].河北建筑科技学院学报, 2006, 23(3): 20-24.
[37] Su, Y., et al. Automatic mesh generation and modification techniques for mixed quadrilateral and hexahedral element meshes of non-manifold models [J], Computer-Aided Design 582 36 (2004) 581–594.
[38] Peng, Q. and Loftus, M. A New Approach to Reverse Engineering Based on Vision Information [J]. International Journal of Machine Tools & Manufacture, 1998, 38: 881-899.
[39] Chazelle, B., Dobkin, D. P., Shouraboura, N., et al. Strategies for polyhedral surface decomposition: An experimental study [J]. Computational Geometry: Theory and Application, 1997, 7(4/5): 327-342.
[40] Hoover, A., Jean-Baptiste, G., and Jiang, X. An experimental comparison of range image segmentation algorithms [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1996, 18(7): 673-689.
[41] Vincent, L. and Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598.
[42] Arora, S., Raghavan, P., and Rao, S. Approximation Schemes for Euclidean k-median and Related Problems [C]. 30th Ann. ACM Symp. Theory of Computing, 1998: 106-113.
[43] Capoyleas, V., Rote, G., and Woeginger, G. Geometric Clustering [J]. Journal of Algorithms, 1991, 12: 341-356.
[44] Shamir, A. Feature space analysis of unstructured meshes [A]. IEEE Visualization, Seattle, Washington, 2003: 185-192.
[45] Hebert, M., Homan, R., and Johnson, A., et al. Sensor-based interior modeling [A]. American Nuclear Society 6th Topical Meeting on Robotics and Remote Systems, Monterey, California, 1995: 731-737.
[46] Trucco, E. and Fisher, R. B. Experiments in curvature-based segmentation of range data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 177-182.
[47] Sagi, K. and Ayellet, T. Hierarchical mesh decomposition using fuzzy clustering and cuts [J]. ACM Transactions on Graphics, 2003, 22(3): 954-961.
[48] Bischoff, S. and Kobbelt, L. Ellipsoid decomposition of 3D-models [A]. International Symposium on 3D Data Processing, Visualization and Transmission, New York, 2002: 480-488.
[49] Li, X., Woon, T. W., Tan, T. S., et al. Decomposing polygon meshes for interactive applications [A]. ACM Symposium on Interactive 3D Graphics, Research Triangle Park, North Carolina, 2001: 35-42.
[50] Lazarus, F. and Verroust, A. Level set diagrams of polyhedral objects [A]. 5th Symposium on Solid Modeling and Applications, New York, 1999: 130-140.
[51] Paul, A. and Debarry. Watersheds: Processes, Assessment and Management [M]. John Wiley & Sons, 2004.
[52] Serra, J. P. Image Analysis and Mathematical Morphology [M]. Academic Press, 1982.
[53] Mangan, A. and Whitaker, R. Partitioning 3D Surface Meshes Using Watershed Segmentation [J]. IEEE Transactions on Visualization and Computer Graphics, 1999, 5(4): 308-321.
[54] Pulla, S. Curvature based segmentation for 3-Dimensional meshes [D]. Tempe, AZ: Arizona State University, 2001.
[55] Rettmann, M. E., Han, X., and Prince, J. L. Automated surface segmentation using Watersheds on the cortical surface [J]. Neuro Image, 2002, 15(2): 329-344.
[56] Marty, K. Segmentation of a human brain cortical surface mesh using Watersheds [R]. Madison: University of Wisconsin, CS766 Final Project, 2002.
[57] Page, D. L., Koschan, A. F., and Abidi, M. A. Perception-based 3D triangle mesh segmentation using fast marching Watersheds [A]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, 2003: 27-32.
[58] Zhang, Y., Paik, J. K., Koschan, A., et al. A Simple and Efficient Algorithm for Part Decomposition of 3-d Triangulated Models Based on Curvature Analysis [A]. International Conference on Image Processing, Rochester, NY, USA, 2002: 273-276.
[59]廖毅,程志全,党岗.一种基于显著性分析的网格分割算法[C].第一届中国图学大会暨第十届华东六省一市工程图学学术年会, 2007.
[60] Garland, M., Willmott, A., and Heckbert, P. S. Hierarchical face clustering on polygonal surfaces [C]. 2001 Symposium on Interactive 3D Graphics, March 19-21, 2001, Research Triangle Park, NC, United states, 2001: 49-58.
[61] Shlafman, S., Tal, A., and Katz, S. Metamorphosis of polyhedral surfaces using decomposition [A]. Annual Conference of the European Association for Computer Graphics, Saarbrücken, 2002: 219–228.
[62] Kanungo, T., et al. An efficient k-means clustering algorithms: Analysis and implementation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 881-892.
[63] Katz, S. and Tal, A. Hierarchical mesh decomposition using fuzzy clustering and cuts [J]. Acm Transactions on Graphics, 2003, 22: 954-961.
[64]陈国栋,李建微,潘林,余轮.基于人体特征三维人体模型的骨架提取算法[J].计算机科学, 2009, 36(7): 295-297.
[65] Leymarie, F. and Kimia, B. The Shock Scaffold for Representing 3D Shape [A]. Proceedings of the 4th International Workshop on Visual Form, Capri, Italy, 2001: 216-228.
[66] Teichmann, M. and Teller, S. Assisted Articulation of Closed Polygonal Models [C]. Proc. 9th Eurographics Workshop on Animation and Simulation, 1998: 254-268.
[67] Lazarus, F. and Verroust, A. Level set diagrams of polyhedral objects [A]. Fifth Symposium on Solid Modeling and Applications, New York, NY, USA, 1999: 130-140.
[68] Verroust, A. and Lazarus F. Extracting Skeletal Curves from 3D Scattered Data [J]. The Visual Computer, 2000, 16(1): 15-25.
[69] Hisada, M., et al. A 3D Voronoi-based Skeleton and Associated Surface Feature [C]. Pacific Conference on Computer Graphics and Applications, 2001: 89-96.
[70] Hilaga, M., Shinagawa, Y., Komura, T. and Kunni. T. Topology matching for fully automatic similarity estimation of 3D shapes [A]. Computer Graphics Proceedings, Annual Conference Series, ACM SIGGRAPH, Los Angeles, California, 2001: 203-212.
[71] Sebastian, T. B., Klein, P. N., Kimia. B. B. Recognition of Shapes by Editing Their Shock Graphs [J]. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26(5): 550-571.
[72] Oda, T., Itoh, Y., Nakai, W., et a1. Interactive Skeleton Extraction using Geodesic Distance [C]. Artificial Reality and Telex stances Workshops, 2006.ICAT’06, 16th International Conference, Nov 2006: 275-281.
[73] Li, X., et al. Decomposing polygon meshes for interactive applications [C]. 2001 Symposium on Interactive 3D Graphics, Research Triangle Park, NC, United states, 2001: 35-42.
[74]孙晓鹏.三维模型的分割与应用研究[D].中国科学院计算技术研究所, 2005.
[75] Shewchuk, J. R. Delaunay refinement algorithms for triangular mesh generation [J]. Computational Geometry-Theory and Applications, 2002, 22: 21-74.
[76]陈圆,王从军,黄树槐.基于STL文件的阶梯剖分算法研究[J].华中科技大学学报, 2004, 32(6): 19-21.
[77] Chan, C. K. and Tan, S. T. Generating assembly features onto split solid models [J]. Computer Aided Design, 2003, 35: 1315-1336.
[78] Delebecque, B., Houtmann, Y., Lauvaux, G., and Barlier, C. Automated generation of assembly features in layered manufacturing [J]. Rapid Prototyping Journal, 2008, 14(4): 234-245.
[79]莫健华.快速成形与快速制模[M].北京:电子工业出版社, 2006.
[80]郭丽华,王从军,黄树槐. CAD文件的STL格式转换[J].应用RP技术的市场商机和解决方案研讨会论文集,武汉,华中科技大学, 2000: 87-92.
[81]徐人平.快速原型技术与快速设计开发[M].北京:化学工业出版社, 2008.
[82] Schaub, D. A. Optimizing Stereolithograph Thought [J]. Journal of Manufacturing Systems, 1997, 16: 290-303.
[83] http://www.autodesk.com/techpubs/autocad/dxf/reference/
[84] Initial Graphics Exchange Specification (IGES) Version 3.0 (U. S.) National Bureau of Standard (NEL) Gaithersburg, ND. PB 86-199757, Apr. 86.
[85] Altemueller J. The STEP file structure. Doc. No.4. 2. 1, Version 12.0 London: ISO TC184/SC4/WG1, 1998: 11-38.
[86] 3D System Inc. Stereolithgraphy Interface Specification [R], July, 1988.
[87]杜忠于,尹希猛,王运赣,黄树槐.快速光造型技术中数据模型的诊断[J].计算机工程, 1995, 21(4): 3-5.
[88]田宗军,李小林,黄因慧.快速成形系统中STL文件的缺陷与修复[J].电加工, 1999, 02: 15-18.
[89]李江峰,钟约先,李电生.一种基于边界缺陷的STL文件修补算法[J].计算机应用, 2002, 31(3): 45-47.
[90]张剑峰,黄因慧,赵剑峰.快速成型系统中STL模型缺陷的自动修复技术[J].扬州大学学报(自然科学版), 2002, 5(2): 33-36.
[91]陈立亮,刘瑞祥,林汉同.基于STL网格剖分技术的研究[J].特种铸造及有色合金, 1999, 1: 145-147.
[92]王丽丽.基于邻域关系的STL文件拓扑重建技术[J].机械制造与研究, 2008, 37(3): 24-29.
[93] Lipman, Y. and Sorkine, O. Linear rotation-invariant coordinates for meshes [J]. ACM Transaction on Graphics, 2005, 24(3): 479-487.
[94] Hradek, J., Kuchai, M., and Skala, V. Hash functions and triangular mesh reconstruction [J]. Computer & Geosciences, 2003, 29: 741-751.
[95] Baumgart, B. G. A polyhedron representation for computer vision[A].National Computer Conference, Anaheim, CA, 1975: 589-596.
[96] Guibas, L. and Stofi, J. Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams [J]. ACM Transactions on Graphics, 1985, 4(3): 74-123.
[97]戴宁,廖文和,陈春美.STL数据快速拓扑重建关键算法[J].计算机辅助设计与图形学学报, 2005, 11(17): 2447-2452.
[98]刘之生,黄纯颖.反求工程技术[M].北京:机械工业出版社, 1992.
[99] Mencl, R. and Muller, H. Interpolation and approximation of surface from threedimensional scattered data points [R]. State of the Art Reports, Eurographics, 1998: 51-67.
[100]张舜德,朱东波,卢秉恒.反求工程中三维几何形状测量及数据预处理[J].机电工程技术, 2001, 30(l): 7-10.
[101]刘立国.点云模型的光顺去噪研究[D].浙江大学硕士学位论文, 2007.
[102] Tubin, G. A signal Processing approach to fair surface design [C]. Proc. of the Computer Graphics. Annual Conf. Series, ACM SIGGRAPH, 1995: 351-358.
[103] Desbrun, M., Meyer, M., Sehroder, P., et al. Implicit fairing of irregular meshes using diffusion and curvature flow [C]. Proc. of the Computer Graphics. Annual Conf. Series, ACM SIGGRAPH, 1999: 317-324.
[104] Perona, P. and Malik, J. Scale-Space and edge detection using anisotropic diffusion [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
[105] Clarenz, U., Diewald, U., and Rumpf, M. Anisotropic geometric diffusion in surface processing [C]. Proc. of the IEEE Visualization, 2000: 397-405.
[106] Tomasi, C. and Manduchi, R. Bilateral filtering for gray and color images [C]. Proc. Of the 6th International Conference on Computer Vision, 1998: 839-846.
[107] Jones, T., Durand, F., and Desbrun, M. Noniterative, feature preserving mesh smoothing [C]. Proc. Of SIGGRAPH03, San Diego, 2003: 943-949.
[108] Fleishman, S., Drori, I., and Cohen-Or, D. Bilateral mesh denoising [C]. Proc. of ACM SIGGRAPH, 2003: 950-953.
[109] Porteous, I. R. Geometric Differentiation [M]. Cambridge University Press, 1994.
[110]徐天长.关于曲面的高斯曲率的应用[J].安庆师范学院学报(自然科学版), 2007, 13(1): 30-31.
[111]欧阳江帆,宋晓冰,袁美玲,柳秀霞.基于均方根曲率和绝对曲率的三维人脸特征定位[J].福建电脑, 2009, 3: 70-71.
[112] Gatzke, T., Grimm, C., Garland, M., and Zelinka, S. Curvature maps for local shape comparison [C]. Shape Modeling and Applications, 2005 International Conference, 13-17 June 2005: 244-253.
[113] Huang, J. and Menq, C. H. Combinatorial manifold mesh reconstruction and optimization from unorganized points with arbitrary topology [J]. Computer-Aided Design, 2002, 34: 149-165.
[114]华刚,郑南宁,薛建儒.基于改进遗传算法的边缘检测阈值自动选取及其应用[J].小型微型计算机系统, 2002, 23(3):318-321.
[115] Luger, G. F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Fifth Edition) [M]. MA, Addison-Wesley, 2004.
[116]王小平.遗传算法理论、应用与软件实现[M].西安:西安交通大学出版社, 2002.
[117] Goldberg, D. E. Genetic Algorithm in Search, Optimization, and Machine Learning [M]. MA, Addison-Wesley, 1989.
[118] Yang, S., Shu, S., and Zhu, S. Feature line extraction from triangular meshes based on STL files [J]. Computer Engineering and Application, 2008, 44(4): 14-19.
[119] Bajaj, C. L. and Dey, T. K. Convex decomposition of polyhedral and robustness [J]. Siam Journal on Computing, 1992, 21(2): 339-364.
[120] Fitzgibbon, A., Pilu, M., and Fisher, R. B. Direct least square fitting of ellipses [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(5): 476-480.
[121] Chen, J. and Han, Y. Shortest path on the polyhedron [C]. Proceedings of the 6th ACM Symposium on Computer Geometry, Berkley, California, 1990: 360-369.
[122] Kanai, T. and Suzuki, H. Approximate shortest path on a polyhedral surface and its applications [J]. Computer-Aided Design, 2001, 33(11): 801-811.
[123] Foley, J. D., van Dam, A., Feiner, S. K., and Hughes, J. F. Computer graphics, Second Edition in C [M]. USA: Pearson Education, 1996.
[124] Zhang, BX and Mouftah, HT. A destination-driven shortest Path tree algorithm [D]. IEEE Communications Society, ed. Proc. of the 2002 IEEE Inter. Conf. on Communications, 2002, 4: 2258-2262.
[125] Langley, P. Elements of Machine Learning [M]. Morgan Kaufmann, 1995.
[126]周志华,王钰.机器学习及其应用2009[M].北京:清华大学出版社, 2009.
[127]刑五丰,易健康.归纳学习方法在结构损伤识别中的比较研究[J].计算机与信息技术, 2006, Z1: 1-3.
[128]洪家荣.归纳学习——算法、理论、应用[M].北京:科学出版社, 1997.
[129] Safavian, S. R. and Landgrebe, D. A survey of decision tree classifier methodology [J]. IEEE Transactions on Systems, Man and Cybernetics, 1991, 21(3): 660-674.
[130] Furnkranz, J. Separate- and-Conquer Rule Learning [J]. Artificial Intelligence Review, 1999, 13: 3-54.
[131] Hothorn, T. and Lausen, B. Bagging tree classifiers for laser scanning images: a data-and simulation-based strategy [J]. Artificial Intelligence in Medicine, 2003, 27(1): 65-79.
[132] Chen, S., Cowan, C. F. N., and Grant, P. M. Orthogonal least squares learning algorithm for radial basis function networks [J]. IEEE Transactions on Neural Networks, 1991, 2(2): 302-309.
[133] Konar, A. Computational Intelligence: Principles, Techniques and Applications [M]. Berlin: Springer, 2004.
[134] Shreiner, D., et al. OpenGL Programming Guide, Fifth Edition: The Official Guide to Learning OpenGL [M].北京:机械工业出版社, 2006.
[135]乔林等.OpenGL程序设计[M].北京:清华大学出版社, 2000.
[136]陈太喜,方亮,刘艳伟,贾龙.基于OpenGL的STL数据模型建模方法[J].机床与液压, 2009, 37(3): 155-158.
[137]徐雪松.STL模型表面点快速拾取技术[J].工程图学学报, 2005, 26(3): 18-22.
[138] Stroud, I. and Xirouchakis, P. C. STL and extensions [J]. Advances in Engineering Software, 2000, 31(2): 83-95
[139] Quinlan J. R. Induction of Decision Trees [J]. Machine Learning, 1986, 1: 81-106.
[140] Hong, J. R. and Mao, C. J. IDAQ: A combination of ID3 and AQ15 [C]. Proceedings of the Second World Congress on Expert Systems, Lisbon, Portugal, 1994: 210-234.
[141]金聪,郭京蕾.人工智能原理与应用[M].北京:清华大学出版社, 2006.
[142]阳明盛,罗长童.最优化原理、方法及求解软件[M].北京:科学出版社, 2009.
[143]林锉云,董加礼.多目标优化的方法与理论[M].吉林:吉林教育出版社, 1992.
[144] Pareto, V. Course Economic Politique. Vol. I and II [A]. Lausanne: Rouge, 1896.
[145] Kuhn, H. W. and Tucker, A. W. Nonlinear Programming [C]. Proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1952.
[146] Garey, M. R., Johnson, D. S., and Sethi, R. The complexity of flowshop and jobshop scheduling [J]. Mathematics of Operations Research, 1976: 117-129.
[147]焦李成.多目标优化免疫算法、理论和应用[M].北京:科学出版社, 2010.
[148]郑金华.多目标进化算法及其应用[M] .北京:科学出版社, 2007.
[149] Steuer, R. E. Multiple Criteria Optimization: Theory, Computation, and Application [M]. New York: Wiley, 1986.
[150] Runarsson, T. P. and Yao. X. Stochastic ranking for constrained evolutionary optimization [J]. IEEE Transaction on Evolutionary Computation, 2000, 4(3):284-294.
[151] Zheng, D., Gen, M., and Cheng, R. Multiobjective optimization using genetic algorithms [J]. Engineering Valuation and Cost Analysis, 1999, 2: 303-310.
[152] Fonseca, C. M. and Fleming, P. J. Genetic algorithm for multiobjective optimization: Formulation, discussion and generation [C]. Proceedings of 5th International Conference on Genetic Algorithms. San Mateo: Morgan Kauffman Publishers, 1993: 416-423.
[153] Srinivas, N. and Deb, K. Multiobjective optimization using non-dominated sorting in genetic algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-248.
[154] Schaffer, J. D. Multiple objective optimization with vector evaluated genetic algorithms [C].Proceedings of 1st International Conference on Genetic Algorithms and Their Applications. Hillsdale: Lawrence Erlbaum Associates, Inc., 1985: 93-100.
[155] Zitzler, E. and Thiele, L. Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach [J]. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271.
[156] Knowles, J. D. and Corne, D. W. Approximating the non-dominated front using the Pareto archived evolution strategy [J]. Evolutionary Computation, 2000, 8(2): 149-172.
[157] [日]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社, 2000.
[158] Coello, C. A., Pulido, G. T., and Lechuga, M. S. Handing multiple objectives with particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279.
[159]郭戈等.快速成形技术[M].北京:化学工业出版社, 2005.
[160]王泉德.任意三角网格模型体积的快速准确计算方法[J].计算机工程与应用, 2009, 45(18): 32-34.
[161]王会凤.复杂模型分割制造关键技术研究及应用[D].上海交通大学博士论文, 2008.
[162]龚奇伟,张李超,莫健华.STL模型自动镂空的算法与应用[J].计算机辅助设计与图形学学报, 2007, 19(1): 54-58.
[163] Holland, W. V. and Bronsvoort, W. F. Assembly features in modeling and planning [J]. Robot Computer Integrated Manufacturing, 2000, 16: 277–294.
[164] De Fazio, T. L. et al. A prototype of feature-based design for assembly [C]. ASME Advanced Design Automation, 1990: 9-16.
[165] Gopi, M., Krishnan, S., and Silva, C. T. Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation [J]. Computer Graphics Forum, 2000, 19(3): 467-478.
[166] Kim, D. S., D. Kim, et al. Voronoi diagram of a circle set from Voronoi diagram of a point set: II. Geometry [J]. Computer Aided Geometric Design, 2001, 18(6): 563-585.
[167] Held, M. Voronoi diagrams and offset curves of curvilinear polygons [J]. Computer-Aided Design, 1998, 30(4): 287-300.
[168] Mayya, N. and Rajan, V. T. An efficient shape-representation scheme using Voronoi skeletons [J]. Pattern Recognition Letter, 1995, 16(2): 147-160.
[169]孙琨,岑启宏,孙蔚.带有装配定位系统的STL模型直接生成算法研究[J].机械制造,2004,42(3): 24-26.
[170] Z-corp公司的Zprinter450成型机. http://www.zcorp.com/en/Products/3D-Printers/ZPrinter-450/spage.aspx