@article{HU2025, 
author = {Guanghua HU and Zhigang DAI and Qinghui WANG},
title = {Machining Feature Recognition Method of B-Rep Model Based on Graph Neural Network},
year = {2025},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {53},
number = {5},
pages = {20-31},
keywords = {machining feature recognition, graph neural network, deep learning, computer-aided design},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.240329},
doi = {10.12141/j.issn.1000-565X.240329},
abstract = {Automatic feature recognition is one of the key technologies of intelligent manufacturing. Traditional rule-based recognition algorithms have poor scalability, and the methods based on deep convolutional networks are of low accuracy because they use discrete models as input and the recognition results are difficult to accurately map back to the original CAD model, causing inconvenience in application. In view of these shortcomings, a feature recognition method based on graph neural network, which can directly analyze B-Rep models, is proposed. The method extracts effective characteristic information and geometric information from the B-Rep structures to form a feature descriptor, and then establishes an adjacency graph with high-level semantic information based on the topological structure of the CAD model. By taking the adjacency graph as the input, an efficient graph neural network model is constructed. By introducing a differentiable generalized message aggregation function and a residual connection mechanism, the model possesses stronger information aggregation performance and multi-level feature capture capabilities. What is more, message normalization strategy is used to ensure the stability of the training process and to accelerate the convergence of the model. After the training, the network can directly classify and annotate all faces in the B-Rep model, thereby realizing feature recognition. Experimental results on the public dataset MFCAD++ demonstrate that the proposed method achieves an accuracy of 99.53% and an average intersection-over-union ratio of 99.15%, which outperforms other similar studies. Further evaluations using more complex testing cases and typical CAD cases from real engineering applications show that the proposed method is of better generalization ability and adaptability.}
}