From CAD Data to Semantic Graphs: A Framework for Classification

The growing complexity of digital assemblies presents significant challenges, particularly in understanding their characteristics and interdependencies. Traditional approaches, which focus largely on geometric properties, fall short in capturing the functional dependencies between components. This research introduces a novel approach using semantic enrichment of CAD components and Graph Neural Networks (GNNs) to classify mechanical parts and analyze their interconnections. By leveraging open-source gearbox designs which are based on native CAD data in addition to STEP data, the methodology showcases the ability of graph-based structures to account for both geometric and functional relationships, providing a more comprehensive digital understanding of assemblies.