中国机械工程 ›› 2022, Vol. 33 ›› Issue (06): 690-697,755.DOI: 10.3969/j.issn.1004-132X.2022.06.008

• 智能制造 • 上一篇    下一篇

基于复杂网络的三维CAD装配模型模块单元发掘

韩周鹏;刘永;巴黎;史慧帆   

  1. 西安理工大学机械与精密仪器工程学院,西安,710048
  • 出版日期:2022-03-25 发布日期:2022-04-21
  • 作者简介:韩周鹏,男,1986年生,讲师。研究方向为三维模型知识发掘与重用、制造信息工程等。发表论文10余篇。E-mail:hanzp@xaut.edu.cn。
  • 基金资助:
    国家自然科学基金(52005404);
    中国博士后科学基金(2020M673612XB);
    陕西省教育厅重点实验室项目(20JS114)

Discovery of Modular Units for Complex Three-dimension CAD Assembly Model Based on Complex Network

HAN Zhoupeng;LIU Yong;BA Li;SHI Huifan   

  1. School of Mechanical and Precision Instrument Engineering,Xian University of Technology,Xi'an,710048
  • Online:2022-03-25 Published:2022-04-21

摘要: 三维CAD模型蕴含丰富的可重用结构知识,为了从已有三维CAD装配模型中提前获得模块知识,促进对复杂三维装配模型的理解与重用,提出了一种基于复杂网络的三维CAD装配模块单元发掘方法。首先融合装配零件的结构、功能、材料关联信息进行零件关联强度综合评价;然后以关联强度矩阵为基础构建三维装配模型所对应的关联关系网络,在此基础上给出基于CNM的社区发现算法实现三维CAD装配模型模块单元发掘;最后以蜗轮蜗杆减速箱三维装配模型为例验证了所提方法的有效性与可行性。

关键词: 三维装配模型, 关联关系网络, 多源关联信息, 社区发现, 模块单元

Abstract: Three-dimension(3D)assembly model embodied plenty of structure knowledge, which might be employed for design reuse. To acquire the modular knowledge from the existing 3D CAD assembly models in advance for promoting the understanding and reuse of complex 3D assembly model, a method for discovering modular units of complex 3D assembly model  was proposed based on complex network. Firstly, the correlation strength among assembly parts was evaluated comprehensively considering assembly structure, function and material similarity. Secondly, the correlation relationship network for describing correlation of assembly parts in 3D assembly model was constructed based on complex network theory. Subsequently, an improved community detection algorithm for discovering modular units knowledge from complex 3D assembly model was given based on CNM (Clauset-Newman-Moore) algorithm. Finally, 3D CAD assembly of worm reduction box was used for verifying the effectiveness and feasibility of the proposed method. 

Key words: 3D assembly model, correlation relationship network, multi-source correlation information, community detection, modular unit

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