基于点云深度学习的加工特征识别方法

Manufacturing feature recognition based on point cloud deep learning

在计算机辅助设计与制造系统中,加工特征识别是一项关键技术。针对传统的特征识别技术可扩展性差、鲁棒性差等问题,提出一种基于点云深度学习的加工特征识别方法。通过对加工特征表面进行均匀点采样,构建加工特征的点云数据集。使用K近邻算法构建点云的旋转不变表示,提出一种融入几何先验知识的点云分类网络。对于多特征模型的点云数据,提出一种加工特征点集的提取方法和相交特征的分离方法。通过具体实例验证了所提方法的有效性,表明该方法能识别CAD模型中的单一特征和相交特征。

In computer aided design and manufacturing systems,the manufacturing feature recognition is a key technology.Aiming at the problems of poor scalability and robustness of traditional feature recognition technology,a manufacturing feature recognition method based on point cloud deep learning was proposed.A point cloud dataset of manufacturing features was constructed by sampling uniformly on the surface of manufacturing features.The K-nearest neighbor algorithm was used to construct a rotation-invariant representation of the point cloud,and a point cloud classification network incorporating geometric prior knowledge was proposed.For the point cloud data of the model with multiple features,an extraction method of the point cloud of manufacturing features and a separation method of intersecting features were proposed.Practical experiments were carried out to demonstrate the effectiveness of the proposed method,and the results illustrated that the method could effectively recognize single features and interacting features for CAD models.