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.

Methods
The Automate dataset contains 451,967 unique CAD parts, 255,211 CAD assemblies, and 1,292,016 unique mates scraped from public OnShape documents.

The data was collected using Onshape's API and pre-processed to detect and remove duplicate and corrupted parts.

Assembly data was processed to flatten sub-assembly hierarchies and to reference deduplicated parts.

Mates were processed to remove duplicates up to identical parts, mate types, and mating coordinate frames.

Document names have been edited to make them compatible with most file systems, and code to convert back to the original Onshape names is provided.