Abstract: Machining feature recognition is of vital significance in computer-aided design(CAD) and computer-aided manufactu-ring(CAM),acting as a critical link that connects CAD and CAM systems.Traditional machining feature recognition methods are primarily categorized into rule-based and learning-based approaches.However,these methods exhibit inherent limitations such as poor generalization capability and high maintenance costs.To address the aforementioned issues,this paper proposes a graph Transformer-based method for machining feature recognition of CAD models.Specifically,the boundary representation(B-Rep) is adopted to describe the geometric and topological structure of CAD models.Face adjacency graphs are utilized to model the local and global spatial relationships of 3D models,while a multi-channel convolutional neural network(CNN) is employed to perform discrete encoding on the geometric attributes of surfaces and curves.Furthermore,the multi-head self-attention mechanism of the graph Transformer is leveraged to dynamically capture long-range dependencies and interactive features between nodes.Finally,a multi-layer perceptron(MLP) is used to realize the classification of machining features.On the public MFCAD++ dataset,the proposed method exhibits exceptional performance,reaching 99.73% in classification accuracy(Acc),99.65% in per-class accuracy,and 99.58% in mean intersection over union(mIoU).Ablation experiments confirm the crucial role of components like the extended adjacency matrix and attribute encoding in ensuring model robustness.Moreover,the integration of domain adaptation significantly improves the model's generalization ability.

Key words: Machining feature recognition, B-Rep representation, Graph Transformer, Multi-layer perceptron, Extended adjacency matrix