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arXiv · 2208.01779

Mates2Motion: Learning How Mechanical CAD Assemblies Work

James Noeckel·Benjamin T. Jones·Karl Willis·Brian Curless·Adriana Schulz
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Abstract

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.

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James Noeckel, Benjamin T. Jones, Karl Willis, Brian Curless, Adriana Schulz. 2022-08-02. Mates2Motion: Learning How Mechanical CAD Assemblies Work. https://arxiv.org/abs/2208.01779

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