基于散乱点云模型的自适应分层方法研究

Adaptive layering method based on the scattered point cloud model

针对在分层过程中,如何调节分层效率与成型精度之间矛盾的问题,通过对现有分层算法,分层过程中初始分层厚度确定、数据精简及轮廓线拟合等方法的研究,提出了一种基于点云距离变化的自适应分层算法。基于散乱点云模型,通过计算每层切片中各线段与最近点的距离,筛选出了距离的最大值;计算了相邻两层之间最大距离的变化率,然后与设定的阈值进行了比较,自动地调整了分层厚度;经过对斗齿点云模型的分层实验,验证了算法的有效性。实验结果表明:该算法能够在一定程度上平衡分层效率和成型精度之间的关系,并且适应于高精度、型面较复杂模型的分层。

Aiming at the problem of stratification efficiency and molding precision in the layering process,an adaptive layering algorithm based on the change of point cloud distance was proposed through the analysis of existing layering algorithms.The study of initial layering thickness determination,data compaction and contour fitting in the process of layering.Based on the scattered point cloud model,the maximum distance was screened by calculating the distance between each line segment and the nearest point in each section.The change rate of the maximum distance between two adjacent layers was calculated,and it was compared with the set threshold value to automatically adjust the thickness of the layers.The validity of the algorithm was verified by the layering experiment of the point cloud model of bucket teeth.The experimental results indicate that the algorithm can balance the relationship between the layering efficiency and the forming accuracy to a certain extent,and it is suitable for the layering of high precision and complex profile models.