基于检索优化的三维特征建模方法研究

项目名称： 基于检索优化的三维特征建模方法研究

项目编号： No.61502124

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 计算机科学学科

项目作者： 高雪瑶

作者单位： 哈尔滨理工大学

项目金额： 20万元

中文摘要： 本项目研究历程无关的三维特征建模方法。利用特征名来标识拓扑面，以邻接面为基础来命名边和点。引入虚拓扑元素与子边的概念来处理拓扑元素消失和不构成几何边界的问题。扩展特征依赖图，使其具有一定的层次结构。在历程无关特征操作中，提出确定特征修改优先级的相关规则，以实现模型的正确重构。以面的形状相似性和邻域结构相似性为基础，使用Ullmann算法进行模型检索。从已有模型中检索可重用部件，并将其用于约束求解，以提高造型效率。借鉴空间刚体运动学的基本定义，以几何实体的欧拉参数表示为基础，建立适用于历程无关建模的约束表达形式。将几何约束转化为代数表达式，将代数方程组的求解视为优化问题。利用粒子群优化算法进行搜索，引入早熟监视机制来观察种群的进化情况，计算种群适应度方差来判断搜索过程是否陷入局部最优。当陷入局部最优时，使用混沌搜索策略进行激活，指导粒子群寻找最优解，提高模型的可编辑性和可修改性。

中文关键词： 历程无关；虚拓扑元素；模型检索；约束求解；粒子群优化

英文摘要： In this task, a 3D history-independent feature modeling method is researched. Topological faces are named by feature names. Topological edges and vertexes are named by their adjacent faces respectively. Virtual topological entities and sub-edges are introduced to solve the problem that topological entities disappear or can not construct geometry boundaries of the model. Feature dependent graph is extended, which makes it have hierarchical structures. In history-independent feature operations, the rules to determine the priorities of feature modification are proposed to reconstruct the model correctly. Ullmann algorithm is applied to model retrieval based on shape similarity and neighbor structure similarity. The reusable parts are retrieved from the existing model and are applied to the modeling process in which the method of constraint solving is utilized to improve the modeling efficiency. The definitions of dimensional rigid body kinematics are used for reference. Based on Euler parameter expressions of geometric entities, constraint expressions suitable to the history-independent modeling are given. Geometric constraints are transformed into algebraic expressions, and the process of solving algebraic equations is regarded as optimization problems. Particle swarm optimization algorithm is used to search solutions. Premature estimation mechanism is introduced to check the evolution of the swarm. Swarm fitness variance is computed to decide whether the particle swarm gets into the local extremum. When the algorithm gets into the local extremum, the chaos search strategy is used to activate particles and search the global best solution. The purpose of this task is to promote the efficiency of editing and modifying models.

英文关键词： history-independent;virtual topological entities;model retrieval;constraint solving;particle swarm optimization