Part machining feature recognition based on a deep learning method

Machining feature recognition is a key step in computer-aided process planning to improve the level of design and manufacturing, production efficiency, and competitiveness. Although the traditional feature recognition method using a graph-based approach has advantages in feature logic expression, the calculation process is inefficient. Deep learning is a new technology that can automatically learn complex mapping relationships and high-level data features from a large amount of data. Therefore, this classification technology has been successfully and widely used in various fields. This study examined a three-dimensional convolutional neural network combined with a graph-based approach, taking advantage of deep learning technology and traditional feature recognition methods. First, the convex and concave machining features of a part were determined using an attributed adjacency graph. Then, the machining features were separated using the bounding box method and voxelized. Subsequently, a stretching and zooming method was proposed to obtain the training data. After training, the test and comparison results demonstrated the high accuracy rate of the proposed method and the improvement in recognition efficiency. The proposed method could also identify convex features, which further improved the recognition range.