AutoMate: a dataset and learning approach for automatic mating of CAD assemblies

Assembly modeling is a core task of computer aided design (CAD),
comprising around one third of the work in a CAD workflow. Optimizing this
process therefore represents a huge opportunity in the design of a CAD
system, but current research of assembly based modeling is not directly
applicable to modern CAD systems because it eschews the dominant data
structure of modern CAD: parametric boundary representations (BREPs). CAD
assembly modeling defines assemblies as a system of pairwise constraints,
called mates, between parts, which are defined relative to BREP topology
rather than in world coordinates common to existing work. We propose
SB-GCN, a representation learning scheme on BREPs that retains the
topological structure of parts, and use these learned representations to
predict CAD type mates. To train our system, we compiled the first
large-scale dataset of BREP CAD assemblies, which we are releasing along
with benchmark mate prediction tasks. Finally, we demonstrate the
compatibility of our model with an existing commercial CAD system by
building a tool that assists users in mate creation by suggesting mate
completions, with 72.2% accuracy.