三维CAD模型公共可重用局部结构自动提取

为了更好地实现CAD模型的重用,提出一种面向CAD模型的自动识别和提取公共可重用局部结构算法.首先将CAD模型用属性化特征邻接图来表示;将公共可重用局部结构的提取转化成频繁子图挖掘问题来解决,通过候选产生、候选剪枝、频繁度计数及后处理等步骤来实现可重用局部结构的提取.实验结果表明,该算法可以实现隐含在外形完全不同的CAD模型中的、不易被发现的局部结构的提取,由于在提取的过程中运用了多种优化算法,因此算法的效率可满足工程应用中的需求.     

面向设计意图不变的三维CAD模型重用方法

为了解决目前检索到的三维CAD模型难以重用的问题,提出一种针对B-rep表示的CAD模型重用方法.首先提出一种自动识别B-rep模型中对称特征的算法,依据面的属性对B-rep模型所有的面进行排序,将具有相同属性的面分为一类,并通过不同类之间的合并形成B-rep模型中的对称特征,同时提取该对称特征的约束知识;然后交互式地定义B-rep模型的其他特征,使B-rep模型中的任一面都属于模型的某一特征(主形状特征或局部形状特征);最后以B-rep模型的主形状特征为特征树的根节点,依据特征之间的邻接关系逐次递归地建立特征之间的依赖关系,并自动创建B-rep模型的特征树.与NXTM同步建模方法进行比较的实验结果表明,文中方法利用模型高层次的设计知识重用已有模型,使模型在修改过程中始终保持原有的设计意图,在模型的重用过程中具有一定的优越性.     

融合制造语义的三维工序模型序列重用方法

针对目前三维工序模型序列重用缺少对加工特征尺寸、加工精度、形位公差等制造信息的考虑,不能很好地支持零件制造过程工艺设计重用的问题,提出一种融合制造信息的三维工序模型序列重用方法.首先,利用图同构原理对工艺路线中三维工序模型序列进行优化,减少搜索空间;然后,利用子图同构对三维工序模型加工特征进行匹配,通过融合制造信息对加工特征匹配进行预处理和相似性评价,以获取具有最相似加工特征的三维工序模型;其次,设计三维工序模型序列优化算法获取同构的三维工序模型序列;最后,以某型轴零件工艺设计过程为例进行实例验证,结果表明融合制造信息的三维工序模型序列重用方法能够准确地根据指定制造特征进行工艺设计重用,具有良好的应用效果.     

三维模型语义检索研究进展

针对近年来三维模型检索研究不只是考虑形状相似性匹配,而更多地关注如何把语义知识应用于检索这一研究新动态,对三维模型语义检索研究现状,包括三维模型知识提取、语义检索中的主动学习机制、三维模型语义检索系统等方面进行总结.在此基础上,对已有研究成果进行比较分析,突出三维模型语义检索取得的进展以及存在的问题.最后,提出三维模型语义检索需要进一步深入研究的方向.     

基于子图同构的三维CAD模型局部匹配

针对整体相似性检索算法无法实施精确的局部结构匹配的问题,提出一种基于子图同构的三维CAD模型局部结构匹配算法.该算法通过提取CAD模型的B-Rep信息,将其表示为以面作为节点的属性邻接图.在局部匹配过程中,用户输入的局部结构被表示成"子图".待匹配的整体CAD模型被表示成"大图";则在整体CAD模型中.检索局部结构的问题就被转换成在"大图"中寻找同构"子图"的问题.子图同构是NP完全问题,通过利用CAD模型的面特征将图顶点有效细分,并利用已匹配顶点之间的邻接关系动态裁剪搜索空间,实现了快速的同构匹配.实验结果表明,该算法能实现精确的局部结构匹配,并且检索效率能满足实际应用要求.     

CAD模型自动语义标注

为了解决基于关键字检索方式难以获得符合设计意图的CAD模型的问题,通过对CAD模型自动添加语义标签来改进传统关键字方式检索CAD模型的准确性.首先提出一种利用属性图比较CAD模型形状相似性的算法,根据图的邻接矩阵及顶点属性构造图顶点集的序列,通过动态编程方法求出图的最大公共子图,得到CAD模型之间的形状相似度;然后根据求出的未知模型与已知模型之间的形状相似度,利用概率方法实现对未知模型的自动语义标注.实验结果表明,采用文中方法可以使基于关键字的检索方法具有搜索形状相似模型的功能,在很大程度上改进了传统关键字方式检索CAD模型的准确性.     

面向设计重用的工程零件设计特征模型构建

随着CAD技术的蓬勃发展和广泛引用,设计重用受到了越来越多的关注,然而由于缺乏有效的底层模型表征,现有的三维CAD模型检索算法普遍存在难以支持设计重用的问题.针对设计重用需求,提出一种设计特征模型的表征结构及构建方法.通过扩展特征树的设计模型表征结构,准确地刻画设计模型中不同粒度、不同形式的特征及特征间的关联关系,有效地捕捉模型中包含的设计语义;通过人工交互定义主形状特征和辅形状特征、系统自动建立特征间关系的方式构建设计特征模型,确保模型表征的唯一性及稳定性.实验结果表明,文中提出的设计特征模型表征结构及构建方法能够有效地捕捉工程零件中包含的设计语义,进而支持面向设计重用的检索.     

局部区分区域在三维模型检索中的应用及改进

由于对三维模型检索查全率与查准率的要求不断提高,在研究模型整体特征的同时,对于模型局部特征的研究越来越多。分析并讨论了几种主流的局部特征提取方法,深入研究了局部区分区域方法,并对此方法做出了改进以适应检索需求,经过试验证明,使用局部区分区域可以提高三维模型检索的查全率和查准率。     

可重用集成设计单元模型研究

设计重用技术的核心是可重用对象的建模问题。该文提出可重用集成设计单元,用它来表达、组织和管理可重用的信息。首先给出了可重用集成设计单元的定义,分析了它的特点;然后建立了可重用集成设计单元的信息模型;随后讨论了可重用集成设计单元的建模技术———面向对象技术及元数据(Meta-data)管理技术。接着提出了两种建立可重用集成设计单元的方法:有目的地建立可重用集成设计单元和从已有资源中挖掘提取可重用集成设计单元。最后,阐述了在PDM系统上通过出版/订阅(Publish/Subscribe)方式建立可重用集成设计单元库的具体实现方法。     

基于知识库的可重用构件检索系统KRR研究

可重用构件的检索问题一直被视为是软件重用的瓶颈问题,虽然目前已提出了不少解决方法,但这些方法都不能令人满意。文章主要讨论了基于知识库的可重用构件检索系统ＫＲＲ,给出了ＫＲＲ系统的总体结构,并对ＫＲＲ系统的核心部分作了详细论述。在ＫＲＲ系统中可以集成多种检索方法,该系统具有简单、易用、易于扩充等特点。     

Design reuse oriented partial retrieval of CAD models

As a huge number of 3D CAD models is generated each year, retrieval of 3D CAD models is becoming more and more important for achieving design reuse. However, the existing methods for partial retrieval of 3D CAD models are very few and far from the requirements of design reuse. In this paper, we present an approach to partial retrieval of 3D CAD models for design reuse. The criteria for determining whether a subpart of 3D CAD models is reusable for design is defined first. Based on the criteria defined, all the design reusable subparts involved in the 3D CAD models in the library are automatically extracted and stored in the library as reference models. Moreover, each design reusable subpart in the library is represented by all its local matching regions in a hierarchical way so as to support multi-mode partial retrieval. In our approach, three partial retrieval modes including normal retrieval, exact retrieval and relaxed retrieval are defined to meet various partial retrieval requirements of design reuse such as the incomplete and vague queries during the early design stage. And the multi-mode partial retrieval is achieved by performing multi-mode matching and similarity assessment between the query and the design reusable subparts in the library indexed by bitmap. Experimental results are presented to demonstrate the effectiveness of the approach.     

Retrieving 3D CAD model by freehand sketches for design reuse

Reuse of existing designs can help companies save up a significant amount of time and money in large-scale product development. Searching out similar CAD models of products is a key to facilitating design reuse. This paper proposes a method for retrieving CAD models of mechanical parts by freehand sketches. Users can draw three2D outline sketches and a skeleton sketch to express their query intent. The 2D outline sketches describe the geometrical information of models and the skeleton sketch contains the topological information. A relevance feedback mechanism is introduced to combine the two similarity degrees measured by 2D outlines or skeleton into a unified one. Experiments are conducted to evaluate the performance of the proposed method.     

基于正视图的三维模型特征提取算法

提出一种基于正视图的三维模型特征提取算法,从前、顶、右、后、底与左分别对模型进行投影,获得6个正视图,使用不同半径的同心圆将每个正视图分割,计算每个同心圆内的高度信息和,得到相邻2个同心圆内高度信息和的差值,每个三维模型特征由 6个正视图描述。实验结果证明,该方法的检索效果与光场法相当,计算速度较快。     

三维模型检索中的特征提取技术综述

介绍了三维模型检索的主要研究内容,综述了三维模型检索中的关键技术——特征提取的研究现状,通过对基于统计特征、骨架几何学的特征提取方法的综合比较与分析,对各种特征提取技术进行了评价．     

累进三维模型相似匹配算法

提出了一种构造三维模型特征二叉树的算法,并根据特征二叉树匹配来获得三维模型的相似程度,特征二叉树与二维模型坐标系统系旋转和平移无关,且适合于三维模型累进牵引匹配,实验结果表明,文中算法能较好地匹配三维模型的相似性。     

A deep learning approach to the classification of 3D CAD models简

Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of the CAD domain, features are selected and extracted from 3D CAD models first, and then pre-processed as high dimensional input vectors for category recognition. By analogy with the thinking process of engineers, a deep neural network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To obtain an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better per-formance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.     

三维CAD模型检索综述

对三维CAD模型检索研究现状进行了深入和系统的综述。CAD模型检索可划分为两个层次:基于视觉相似性的三维CAD模型检索、面向语义与功能描述的三维CAD模型检索。分别分析了两种检索层次下CAD模型的检索方法。在此基础上总结了三维CAD模型检索评测基准库、评测方法等。最后展望了现有研究中的难点及进一步的研究方向。     

Common design structures and substitutable feature discovery in CAD databases

It has been widely reported that the reuse of previously created components, or features, in new engineering designs will improve the efficiency of a company’s product development process. Although the reuse of engineering components has established metrics and methodologies, the reuse of specific design features (e.g. stiffening ribs, hole patterns or lubrication grooves, etc.) has received less attention in the literature. Typically, researchers have reported approaches to partial design reuse that identify patterns predominately in terms of geometrically similar shapes (i.e. a set of features) whose elements are adjacent, cohesive, and decoupled from the overall form of a component.In contrast, this paper defines a common design structure (CDS) as collections of frequently occurring features (e.g. holes) with common parametric values (e.g. diameters) in a CAD database (irrespective of their locations or spatial connectivity between other features on a component). By exploiting the established data-mining technology of association rules and item-sets the authors show how CDSs can be efficiently computed for hundreds of 3D CAD models. A case study, with hole data extracted from a publicly available dataset of hydraulic valves, is presented to illustrate how item-sets associated with CDS can be computed and used to support predictive design by identifying potentially ‘substitutable features’ during an interactive design process. This is done using a combination of association rules and geometric compatibility checks to ensure the system’s suggestion are implementable. The use of the Kullback–Leibler divergence to assess the degree of similarity between components is identified as a crucial step in the process of identifying the “best” suggestions. The results illustrate how the prototype implementation successfully mines the CDSs and identifies substitutable hole features in a dataset of industrial valve designs.