Automated feature recognition (AFR) plays an important role in automated measurement path planning and metrological data processing in the manufacturing industry. Existing AFR methods face critical limitations, such as the loss of geometric-topological fidelity during Computer-aided design (CAD) model conversion and inadequate instance segmentation for dimensional metrology. To address these challenges, we propose an integrated multi-attention and multi-strategy network (MMNet) for feature recognition, which mainly includes the multi-attention geometric and attribute fusion module (MGAM) and the multi-strategy semantic and instance segmentation module (MSIM). Specifically, MGAM employs multi-attention mechanisms to synergize local geometric features with global attributes of the boundary representation (B-rep) to enhance recognition precision. Subsequently, MSIM integrates a graph neural network for face-level semantic segmentation with rule-based instance clustering to achieve robust feature recognition. Experimental results demonstrate that our proposed method outperforms other state-of-the-art methods on MFCAD, MFCAD++, and our real-world datasets.
Ma et al. (Thu,) studied this question.