Abstract: Machining feature recognition is crucial in computer-aided design (CAD) and manufacturing (CAM) as it serves as a vital link between CAD and CAM systems. Researchers have proposed two types of machining feature recognition methods: rule-based and learning-based, with the latter showing superior performance and garnering more attention. However, existing recognition methods face challenges such as insufficient utilization of geometric information, inaccurate machining feature localization, and complexity in the instance segmentation process. To address these issues, this paper proposes PT-MFR, a CAD model machining feature recognition method based on Point Transformer. It performs two tasks: semantic segmentation and instance segmentation, predicting the semantic category of machining features for each face and calculating face similarity to segment machining feature instances. The results of both tasks are integrated to obtain the machining feature recognition results. Experimental results demonstrate that the proposed method outperforms other methods.

Key words: machining feature recognition, point cloud, neural network, Point Transformer