Boundary representation compatible feature recognition for manufacturing CAD models

Abstract

This paper presents a Boundary Representation (BREP) compatible data representation for Graph Neural Network (GNN) based feature identification. This data representation follows BREP and STEP AP 203 standards and can transfer holistic manufacturing CAD information to the deep neural network, which assists to identify small local geometrical features and highly complex interactive geometric features. Inversely, this data representation can be easily converted back to a conventional CAD model due to its direct encoding approach. With this data representation, the GNN can reach 99.57% accuracy on 36 classes FeatureNet + dataset and realize 99.12% accuracy on Machining Process Identification (MPI) dataset with highly interactive features. This method can largely benefit Computer Assisted Process Planning and pave the way for future industrial automation.