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Automatic Recognition of Machining Features Based on Point Cloud Data Using Convolution Neural Networks

Automatic recognition of machining features is the key technology to realize the integration of CAD/CAM. With the development of intelligent manufacturing technology, it has important significance to automatically recognize machining features from parts represented by real-time point clouds. At the same time, it puts new challenges for feature recognition technology. In this paper, based on the 3D point cloud data of part models and PointNet architecture, an approach and data structure for automatic recognition of machining features using convolution neural networks (CNN) is proposed. A sample library for learning 3D point cloud data is constructed by CAD model transformation and feature sampling. The presented CNN recognition system can recognize twenty-four kinds of machined features by sample training and recognition experiments. The recognition accuracy rate is more than 95%. This approach makes full use of the invariance of transformation of the point cloud model, and reduces the unnecessary data that is used by conventional 3D voxel network models. The presented method is simple and efficient, and has good robustness to the point cloud disturbance and noise.