快速成型中散乱数据点云的自适应分层算法研究

Research on the Algorithm of Self-adapting Slicing for Scattered Data Points in Rapid Prototyping

提出了一种快速成型中散乱数据点云的自适应分层算法,该算法根据分层厚度阈值对散乱数据点云进行空间栅格划分,采用八叉树组织其空间拓扑结构,基于该结构获取层面邻域数据,通过其曲率分布特征自适应调整分层厚度,根据层面邻域数据与分层平面的交点获取分层数据。实验证明,该算法数据适应性强,分层数据获取准确性高,可快速有效地实现散乱数据点云的自适应分层处理。

A algorithm of self-adapting slicing for scattered data points in rapid prototyping was proposed. The algorithm divides the space of the scattered data points by threshold of layered thickness, organizes special topology structure by octree, obtains the neighborhood data by special topology, adjusts the layered thickness by the curvature distributing feature of neighborhood data, fourth, and obtains the layer data by the intersection of neighborhood data with layer plane. Experimental examples show that this algorithm can realize the self-adapting slicing of scattered data points quickly and availably, can obtain the layer data and has strong adaptability of data type.