采用图分解的特征识别算法研究

CAD/CAE模型转换，其关键在于如何将模型分解为最简单元，这些单元往往具有相近的网格划分属性，可以方便估计计算误差和计算时间。基于此提出了基于图分解的特征识别算法，对属性邻接图进行分解，根据分解后的属性邻接图中的连通分量生成体特征。该算法不再局限于特征类型，只要合理控制顶点的可分解性判断就可以得到期望的模型分解结果；同时该算法可以获得体特征，使得可以在特征这一粒度上进行特征删除和替换，以方便地完成模型的简化。

Study on Feature Recognition Algorithm Based on Graph Decomposition

The key problem of CAD/CAE model transformation lies in how to decompose models to the simplest elements. Such element owns similar gridding property which can weigh computing error and time. And for this reason this paper proposed a feature recognition algorithm based on graph decomposition and decomposed the attributed adjacent graphs to many connectivity components, and then formed the volumetric feature in terms of these connectivity components. This algorithm is not limited to the feature type; it would return a desired result of model decomposition only if the resolvability of the vertex is controlled reasonably. And meanwhile, by using this algorithm we could acquire entity characteristics and remove or replace feature in the level of characteristics, which can simplify the model conveniently.