Topo-Geom DualGNN: A Dual-Graph Fusion Network for Machining Feature Recognition

Machining feature recognition is a key enabling technology in intelligent manufacturing that extracts manufacturing semantics from the boundary representation (B-Rep) of 3D CAD models to bridge design and process planning. Recent advances in deep learning have accelerated data-driven feature recognition methods. Among these, graph neural networks (GNNs) have gained significant attention due to their natural compatibility with the non-Euclidean, hierarchical topological structure of B-Rep data, enabling efficient and lossless encoding of geometric and topological attributes. However, existing GNN-based methods primarily leverage the topological structure and geometric attributes of B-Rep models, often neglecting the inherent geometric relationships present in the B-Rep data structure. To address this gap, we propose a dual-graph fusion network (Topo-Geom DualGNN) that integrates a topological attribute adjacency graph and a geometric relationship graph. Our approach employs a GatedGCN-based graph encoder and an FiLM-based cross-stream fusion mechanism to jointly encode topological and geometric information from the B-Rep model. Evaluations on open-source synthetic datasets, including MFInstSeg and MFRCAD, demonstrate that the proposed method achieves competitive comprehensive recognition performance and exhibits promising capability in recognizing machining features in complex parts.