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.
@article{arxiv.2208.01779,
title = {Mates2Motion: Learning How Mechanical CAD Assemblies Work},
author = {James Noeckel and Benjamin T. Jones and Karl Willis and Brian Curless and Adriana Schulz},
journal= {arXiv preprint arXiv:2208.01779},
year = {2023}
}
Comments
Contains 5 pages, 2 figures. Presented at the ICML 2022 Workshop on Machine Learning in Computational Design