Adaptive recognition of machining features in sheet metal parts based on a graph class ...

Summary

The article discusses the use of Graph Neural Networks (GNNs) for recognizing machining features from B-Rep CAD models, incorporating attributes and topological information within the framework, and applying advanced techniques to improve learning, stability, and generalization in an incremental learning context. The tags 'Metric tensor', 'Time in physics', 'Dot', 'General relativity', and 'Artificial neural networks' indicate a broader context of mathematical and physical concepts that relate to the multidimensionality and structural complexity addressed by the proposed Sheet-metalNet GNN in the domain of computer-aided design and manufacturing.