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飞机框肋类零件基础特征自动识别与提取算法

汤志鸿 郑国磊 郑艺玮

汤志鸿, 郑国磊, 郑艺玮等 . 飞机框肋类零件基础特征自动识别与提取算法[J]. 北京航空航天大学学报, 2019, 45(4): 811-820. doi: 10.13700/j.bh.1001-5965.2018.0454
引用本文: 汤志鸿, 郑国磊, 郑艺玮等 . 飞机框肋类零件基础特征自动识别与提取算法[J]. 北京航空航天大学学报, 2019, 45(4): 811-820. doi: 10.13700/j.bh.1001-5965.2018.0454
TANG Zhihong, ZHENG Guolei, ZHENG Yiweiet al. Automatic recognition and extraction algorithm for basic features of aircraft sheet metal parts[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(4): 811-820. doi: 10.13700/j.bh.1001-5965.2018.0454(in Chinese)
Citation: TANG Zhihong, ZHENG Guolei, ZHENG Yiweiet al. Automatic recognition and extraction algorithm for basic features of aircraft sheet metal parts[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(4): 811-820. doi: 10.13700/j.bh.1001-5965.2018.0454(in Chinese)

飞机框肋类零件基础特征自动识别与提取算法

doi: 10.13700/j.bh.1001-5965.2018.0454
详细信息
    作者简介:

    汤志鸿  男, 硕士。主要研究方向:CAD/CAM

    郑国磊  男, 博士, 教授, 博士生导师。主要研究方向:智能 CAD、工装智能设计

    郑艺玮  男, 学士。主要研究方向:CAD/CAM

    通讯作者:

    郑国磊, E-mail: zhengguolei@buaa.edu.cn

  • 中图分类号: V260.5

Automatic recognition and extraction algorithm for basic features of aircraft sheet metal parts

More Information
  • 摘要:

    飞机框肋类零件是组成飞机骨架的重要零件,具有数量大、形状各异等特点,其生产制造所耗费的时间在飞机研制过程中占有较大比重。然而,通过现有CAD软件所提供的功能进行相关制造操作,无论是效率、质量等均已不能满足现代飞机设计和制造要求,围绕飞机框肋类零件研究和开发相关的自动化制造系统已迫在眉睫。基于框肋类零件边界表示模型对零件基础特征进行自动识别与提取,是实现后续相关工艺规划与加工的基础与前提。针对该问题,提出零件基础特征模型,并建立一种基于同侧面的特征识别算法,即:以零件STEP数据作为输入,选取两侧腹板面,应用属性邻接图(AAG)构建、有效邻面识别、关联面完整识别等方法,逐级识别各级关联面以构建两侧同侧面,通过同侧面单元匹配最终实现基础特征构造和特征邻接图构建。其中,针对零件三维模型中的碎面缺陷提出其定义与识别方法,以保证特征面识别的完整性。经由实例测试,验证所提算法的可行性与有效性。

     

  • 图 1  飞机框肋类零件基础结构

    Figure 1.  Basic structure of aircraft sheet metal parts

    图 2  常见组合面

    Figure 2.  Common composite faces

    图 3  广义面环

    Figure 3.  Loops in generic faces

    图 4  基础特征

    Figure 4.  Basic feature

    图 5  飞机框肋类零件基础特征邻接图

    Figure 5.  Adjacency graph of aircraft sheet metal part's basic features

    图 6  基础特征识别算法流程图

    Figure 6.  Flowchart of basic feature recognition algorithm

    图 7  边凹凸性计算

    Figure 7.  Calculation of edge's concavity-convexity

    图 8  过渡面及与相邻面拓扑关系

    Figure 8.  Transitional surface and its topological

    图 9  零件碎面

    Figure 9.  Fragmentary faces of part

    图 10  测试对象1及运行结果

    Figure 10.  Test part 1 and test result

    图 11  测试对象2及运行结果

    Figure 11.  Test part 2 and test result

    表  1  零件同侧面

    Table  1.   Same-side faces of parts

    同侧面 同侧面单元 示意图 拓扑关系
    关键面
    1级关键面
    2级关键面
    下载: 导出CSV

    表  2  测试对象1基础特征识别结果统计

    Table  2.   Statistics of basic feature recognition results for test part 1

    识别结果 关键特征 1级关联特征 2级关联特征 3级关联特征 4级关联特征
    总数 1 1 14 14 14 14 11 11 20 20
    正确 14 14 14 14 11 11 20 20
    错误 0 0 0 0 0 0 0 0
    正确率/% 100 100 100 100 100 100 100 100
    下载: 导出CSV

    表  3  测试对象2基础特征识别结果统计

    Table  3.   Statistics of basic feature recognition results for test part 2

    识别结果 关键特征 1级关联特征 2级关联特征 碎面缺陷 过渡面
    总数 1 1 2 2 3 3 3 2 3 3
    正确 2 2 3 3 3 2 3 3
    错误 0 0 0 0 0 0 0 0
    正确率/% 100 100 100 100 100 100 100 100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-31
  • 录用日期:  2018-10-26
  • 网络出版日期:  2019-04-20

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