用自然语言生成3D建模脚本，让AI自动完成复杂设计任务。
Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling
- 通过自然语言输入生成CAD初始脚本，结合错误反馈迭代优化。
- 简单到中等复杂度设计成功率高，高度约束模型需多次修正。
- 适合想快速原型设计的工程师，或希望降低建模门槛的用户。
大型语言模型（LLMs）正推动各行业在效率、可扩展性和创新性上的变革。本文探索将LLMs与FreeCAD结合，用于自动化计算机辅助设计（CAD）工作流。传统CAD流程复杂且依赖专业绘图技能，不利于快速原型设计与生成式设计。我们提出一种框架：由LLM根据自然语言描述生成初始CAD脚本，并基于错误反馈进行迭代执行与优化。通过一系列逐步增加复杂度的实验评估该方法效果。结果表明，LLMs在简单至中等复杂度设计上表现良好，但在高度约束模型上仍需多次修正。研究强调需改进记忆检索、自适应提示工程及混合AI技术以提升脚本鲁棒性。未来方向包括集成云端执行与探索更先进的LLM能力，进一步推进CAD自动化。该工作突显了LLMs在设计流程中的变革潜力，同时指明了关键发展领域。
原文摘要 · Abstract (English)
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.
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