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复杂结构件CAD模型碎面缺陷自动识别与修正方法 被引量:1

An Automatic Fragment Face Error Recognition and Correction Technique for Complex Structural CAD Model
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摘要 针对复杂结构件数模中常存在碎面缺陷,易导致零件在数控加工自动编程时出现特征识别错误、特征提取困难等问题,提出基于属性邻接图的自动识别与修正方法.首先分析复杂结构件碎面缺陷的特征,给出碎面缺陷的定义;然后建立结构件CAD模型的有效属性邻接图,计算面和边的属性并对图中元素赋值,识别碎面缺陷;再根据碎面的几何类型和几何参数构造相应类型的基面,并对基面进行拟合完成碎面缺陷的修正;最后给出文中方法的实现流程并开发了相应的算法,结合实例证明了该方法的正确性和有效性.文中算法已在CATIA V5平台上实现,并集成在飞机复杂结构件快速数控编程系统中,应用于实际生产,取得了良好的效果. There are always fragment errors in complex structural CAD model,which is prone to bringing troubles into the successive machining operations.To solve this problem,an automatic fragment face error recognition and correction technique based on attribute adjacency graph is proposed.First,the characteristics of fragment face error in complex structural model are analyzed and the definition of the fragment face.error is given.Second,the attribute adjacency graph of the structural part model is constructed.And the attributes of the faces and edges of the graph are calculated to assign the values to elements of the graph.Next,the fragment face error is identified in the form of extended attribute adjacency graph.Then,the relative basic surface of fragment face error is constructed based on its geometric type and parameters.And the basic surfaces are fit to correct the errors.Finally,the algorithm flow of this technique is given and implemented.The experimental results are showed to prove that the presented method is both correct and effective.
作者 周敏 郑国磊 刘远 Zhou Min;Zheng Guolei;Liu Yuan(College of Engineering,China Agricultural University,Beijing 100083;School of Mechanical Engineering and Automation,Beihang University,Beijing 100191)
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2018年第11期2174-2181,共8页 Journal of Computer-Aided Design & Computer Graphics
基金 辽宁省自然科学基金(航空联合基金)(2015022005) 中央高校基本科研业务费专项资金(2018QC049)
关键词 数控编程 碎面缺陷 CAD模型修复 飞机结构件 NC programming fragment face error CAD model repair aircraft structure
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  • 1刘勇,徐从富,陈卫东,潘云鹤.带圆弧简单多边形的面积公式获取算法[J].计算机辅助设计与图形学学报,2005,17(4):748-753. 被引量:4
  • 2周敏,邓学雄,陈君梅.UG二次开发技术及其应用[J].工程图学学报,2005,26(5):42-44. 被引量:10
  • 3王宏涛,张丽艳,李忠文,刘胜兰,周儒荣.基于RBF神经网络的三角网格曲面孔洞修补[J].中国机械工程,2005,16(23):2072-2075. 被引量:20
  • 4尹卫星,胡青泥,齐晓松,吴金来.改进CAD模型数据质量的研究[J].机械工程师,2006(2):105-107. 被引量:10
  • 5GB/T18784-2002,CAD/CAM数据质量[S].中华人民共和国国家标准.
  • 6Yang J,Han SH,Park SH.A method for verification of CAD model errors[J].J Eng Des 2005,16(3):337-352.
  • 7Yang J,Han SH,Park SH,et al.Investigation of product data quality in the Korean automotive industry[J].Trans Soc CAD CAM Eng,2004,10(4):274-283.
  • 8SASHIKUMAR V,MILIND S,RAHUL R,et al.Blend recognition algorithm and applications[C]//Proeeeding of the 6th ACM Symposium on Solid Modeling and AApplications.New York,N.Y.,USA:ACM,2001:99-108.
  • 9ZHU H,MENQ C H.B-Rep model simplification by automatic fillet/round suppressing for efficient automatic feature recognition[J].Computer-Aided Design,2002,34(2):109-123.
  • 10LIU Xiaojun,YI Hong,NI Zhonghua,et al.Recognizing 2.5D manufacturing feature using neural network[C]//Proceedings of the 15th International Conference on Mechatronics and Machine Vision in Practice.Washington D.C.,USA:IEEE,2008:311-316.

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