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Found 6 papers, 5 papers with code
DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation
This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requirements in technical documentation.
Conceptual Design Generation Using Large Language Models
Expert evaluations indicate that the LLM-generated solutions have higher average feasibility and usefulness while the crowdsourced solutions have more novelty.
What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files
In this work we propose that the natural language names designers use in Computer Aided Design (CAD) software are a valuable source of such knowledge, and that Large Language Models (LLMs) contain useful domain-specific information for working with this data as well as other CAD and engineering-related tasks.
Material Prediction for Design Automation Using Graph Representation Learning
Successful material selection is critical in designing and manufacturing products for design automation.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
Classifying Component Function in Product Assemblies with Graph Neural Networks
Function is defined as the ensemble of tasks that enable the product to complete the designed purpose.
