Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling

TL;DR

This paper explores using Large Language Models to automate CAD design by generating scripts from natural language, demonstrating promising results for simple models but highlighting challenges with complex designs.

Contribution

It introduces a framework integrating LLMs with CAD workflows to automate script generation from natural language descriptions, advancing CAD automation techniques.

Findings

LLMs perform well on simple to moderate designs

Multiple refinements are needed for complex models

Future improvements include better memory retrieval and hybrid AI methods

Abstract

Large Language Models (LLMs) are revolutionizing industries by enhancing efficiency, scalability, and innovation. This paper investigates the potential of LLMs in automating Computer-Aided Design (CAD) workflows, by integrating FreeCAD with LLM as CAD design tool. Traditional CAD processes are often complex and require specialized sketching skills, posing challenges for rapid prototyping and generative design. We propose a framework where LLMs generate initial CAD scripts from natural language descriptions, which are then executed and refined iteratively based on error feedback. Through a series of experiments with increasing complexity, we assess the effectiveness of this approach. Our findings reveal that LLMs perform well for simple to moderately complex designs but struggle with highly constrained models, necessitating multiple refinements. The study highlights the need for improved memory retrieval, adaptive prompt engineering, and hybrid AI techniques to enhance script robustness. Future directions include integrating cloud-based execution and exploring advanced LLM capabilities to further streamline CAD automation. This work underscores the transformative potential of LLMs in design workflows while identifying critical areas for future development.