CADDesigner: Conceptual Design of CAD Models Based on General-Purpose Agent
This addresses the need to lower entry barriers and improve efficiency for industrial manufacturing designers, though it appears incremental as it builds on existing LLM and feedback techniques.
The paper tackles the problem of high expertise requirements in CAD design by introducing an agent that uses large language models to generate CAD modeling code from textual descriptions and sketches, achieving state-of-the-art performance in CAD code generation.
Computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing but typically requires a high level of expertise from designers. To lower the entry barrier and improve design efficiency, we present an agent for CAD conceptual design powered by large language models (LLMs). The agent accepts both abstract textual descriptions and freehand sketches as input, engaging in interactive dialogue with users to refine and clarify design requirements through comprehensive requirement analysis. Built upon a novel Context-Independent Imperative Paradigm (CIP), the agent generates high-quality CAD modeling code. During the generation process, the agent incorporates iterative visual feedback to improve model quality. Generated design cases are stored in a structured knowledge base, enabling continuous improvement of the agent's code generation capabilities. Experimental results demonstrate that our method achieves state-of-the-art performance in CAD code generation.