What’s In A Name?

Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files

The natural language names designers use in CAD software are a valuable source of semantic knowledge.

This work investigates the value in the natural language part and document names users provide when they create CAD models. In a first step towards multi-modal text-CAD learning, our results show that Large Language Models are able to leverage the noisy text data to predict part-part and part-whole relationships, with direct applications in automations and recommendations for part re-use, auto-complete, assembly categorizations, smart tool suggestions and library part recommendations.

Published in the Journal of Computing and Information Science in Engineering.

View publication

Related Resources



Vice VRsa: Balancing Bystander’s and VR user’s Privacy through Awareness Cues Inside and Outside VR

Informing VR users about bystander presence and bystanders about the…



Leveraging Robotics for Cleaner Construction Jobsites

The value in Spot’s ability to execute repeatable, autonomous missions…



CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly

We introduce CAPRI-Net, a self-supervised neural net-work for learning…



Novel Input & Output

The nature and quality of interaction can be dramatically affected by…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us