北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (4): 811-820.doi: 10.13700/j.bh.1001-5965.2018.0454

• 论文 • 上一篇    下一篇

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   

  1. 北京航空航天大学 机械工程及自动化学院, 北京 100083
  • 收稿日期:2018-07-31 出版日期:2019-04-20 发布日期:2019-04-26
  • 通讯作者: 郑国磊 E-mail:zhengguolei@buaa.edu.cn
  • 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。

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

TANG Zhihong, ZHENG Guolei, ZHENG Yiwei   

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing 100083, China
  • Received:2018-07-31 Online:2019-04-20 Published:2019-04-26

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。

关键词: 数字化制造, 飞机框肋类零件, 特征识别, 实体模型, 特征表达

Abstract: Aircraft sheet metal part is an important part of the aircraft structure, which has the characteristics of large quantity and shape variety. The time consumed in the production process of aircraft sheet metal parts occupies a large proportion in the process of aircraft development. Current production process through the functions provided by the existing CAD software cannot meet the requirements of modern aircraft design and manufacture in terms of efficiency and quality. Research and development of relevant automatic design and manufacturing system for aircraft sheet metal parts have become an urgent demand. Recognition and extraction of basic features of parts based on B-rep model are the basis and premise for subsequent related process planning and manufacturing. Aimed at this, this paper proposes the basic feature model of parts and presents a feature recognition algorithm based on same-side face. That is, with the STEP data as input, the web faces on both sides of parts are selected. Using the methods of attribute adjacency graph (AAG) construction, effective-adjacent faces recognition and complete recognition of the correlative faces, the correlative faces at all levels are recognized step by step to construct the same-side faces on both sides. Finally, the basic features and their adjacency graph are constructed by matching of the same-side unit. In order to ensure the integrity of feature faces, the definition and recognition method of fragmentary face defects in 3D part model are presented. Examples are given to illustrate the feasibility and effectiveness of the approach.

Key words: digital manufacturing, aircraft sheet metal part, feature recognition, solid model, feature representation

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