AgentsCAD: 通过多智能体大语言模型推理和几何特征识别实现FDM零件的自动化制造设计
AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition
AI总结：
提出AgentsCAD多智能体系统，结合B-Rep几何解析与LLM推理，自动检测FDM零件悬垂缺陷并生成修改建议，输出修正STEP文件。
摘要：
使用熔融沉积建模（FDM）制造的零件通常需要进行增材制造设计（DFAM）修改，以确保可打印性、结构完整性并减少后处理。当前的切片软件能够识别陡峭悬垂等缺陷，但无法修改底层几何形状。本文提出AgentsCAD，一个多智能体系统，它桥接了原始边界表示（B-Rep）几何与大语言模型（LLM）推理，以自动化目标DFM。工作流程从解析STEP文件开始。智能体系统检测超过45°阈值的悬垂，构建面邻接拓扑图，并可选择注入从MFCAD++（59,665个零件）训练的GraphSAGE模型生成的语义特征标签，然后调度Claude Sonnet设计推理智能体，推荐重新定向、圆角、倒角等修改。GPT-4o视觉语言验证器检查渲染视图以确认几何完整性。输出包括修改后的STEP文件和人类可读报告。在鸟舍模型上的测试案例表明，该系统正确诊断悬垂，选择合适的缺陷缓解策略，并提出物理上有效的修正，部分解决了LLM驱动CAD修改中核心的几何到语言翻译问题。
英文摘要：
Parts manufactured with Fused Deposition Modeling (FDM) often require Design for Additive Manufacturing (DFAM) modifications to ensure printability, structural integrity, and reduced post-processing. Current slicers identify defects such as steep overhangs but are unable to modify the underlying geometry. This work presents AgentsCAD, a multi-agent system that bridges raw boundary-representation (B-Rep) geometry and Large Language Model (LLM) reasoning to automate targeted DFM. The workflow begins by parsing a STEP file. The agentic system detects overhangs above a 45°threshold, constructs a face-adjacency topology graph, and optionally injects semantic feature labels from a GraphSAGE model trained on MFCAD++ (59,665 parts), before dispatching a Claude Sonnet design-reasoning agent that recommends reorientations, fillets, chamfers, and similar modifications. A GPT-4o vision-language verifier inspects rendered views to confirm geometric integrity. Outputs include a modified STEP file and a human-readable report. A test case on a birdhouse model demonstrates that the system correctly diagnoses overhangs, selects appropriate defect mitigation strategies, and proposes physically valid corrections, partially solving the geometry-to-language translation problem central to LLM-driven CAD modification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
