Mates2Motion: Learning How Mechanical CAD Assemblies Work
This work addresses the challenge of understanding mechanical motion in CAD assemblies for engineers and designers, representing an incremental improvement in automating assembly analysis.
The researchers tackled the problem of inferring degrees of freedom in mechanical CAD assemblies by developing a deep learning model trained on a large dataset of real-world assemblies, resulting in methods to refine mates and narrow motion axes, with a user study creating a motion-annotated test set for evaluation.
We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations. We train our model using a large dataset of real-world mechanical assemblies consisting of CAD parts and mates joining them together. We present methods for re-defining these mates to make them better reflect the motion of the assembly, as well as narrowing down the possible axes of motion. We also conduct a user study to create a motion-annotated test set with more reliable labels.