机械3维CAD模型的聚类和检索  

Clustering & retrieval of mechanical 3D CAD models

作　　者：王玉[1] 马浩军[2] 何玮[2] 肖煜中[2] 周雄辉[2] 

机构地区：[1]同济大学中德工程学院,上海200070 [2]上海交通大学国家模具CAD工程研究中心,上海200030

出　　处：《计算机集成制造系统》2006年第6期924-928,934,共6页Computer Integrated Manufacturing Systems

基　　金：高等学校博士点基金资助项目(20020248017)~~

摘　　要：为了弥补传统的基于属性检索方法的缺陷和不足,真正实现机械3维CAD模型基于几何内容的聚类和检索,提出了一种基于内容的机械3维CAD模型的聚类和检索方法。首先,基于查找关键字的方法,将CAD模型的产品模型数据交换标准AP203 Part21文件转换为属性图文件;其次,进行属性图的相关属性计算,提取特征不变量,并结合属性图的节点和边的相关属性形成CAD模型的特征不变矢量;最后,用特征不变矢量作为自组织特征映射神经网络的输入,利用其保拓扑性对CAD模型进行聚类分析。基于60种工业实用CAD模型对该方法进行了实验验证,结果表明,所提方法可行有效,能够满足一般工程检索的需要。To overcome the shortcomings of traditional attribute-based retrieval method and to realize geometricalcontent-based retrieval for mechanical three-dimensional （3D） CAD models, a new approach of clustering and retrieval of mechanical 3D CAD models was presented. Firstly, The STandard for the Exchange of Product model data （STEP） AP203 Part21 files of CAD model were transformed into attributed-graph files by searching and matching keywords. Secondly, feature invariants were extracted and feature invariant vector of CAD model was formed by calculating graph related attributes such as the total number of nodes and edges. Finally, a Self-Organization feature Mapping （SOM） neural network model was employed to cluster and retrieve CAD models by using the extracted invariant vector as its input to train the neural network. The proposed approach was verified to be valid and feasible based on 60 real industry 3D CAD models, and the experimental results showed that it could meet general requirements of engineering retrieval.

关 键 词：相似性评估 自组织特征映射神经网络 聚类 检索 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]