基于LLM辅助推理的上下文感知2D绘图注释到3D CAD特征映射
Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation
AI总结：
本文提出一种基于LLM辅助推理的上下文感知框架，实现2D绘图注释到3D CAD特征的映射，提升制造自动化精度和可靠性。
摘要：
制造自动化在工艺规划、检验规划和数字线程集成中依赖于一种统一的规范，该规范将3D CAD模型的几何特征与对应的2D工程图中的几何尺寸和公差（GD&T）标注、基准定义和表面要求联系起来。尽管基于模型的定义（MBD）允许这些规范直接嵌入到3D模型中，但2D图纸仍然是汽车、航空航天、造船和重型机械行业制造意图的主要载体。正确地将绘图注释与相应的3D特征联系起来是困难的，因为存在上下文模糊性、重复的特征模式以及对透明和可追溯决策的需求。本文提出了一种确定性优先、上下文感知的框架，将2D绘图实体映射到3D CAD特征，以生成统一的制造规范。绘图标注首先进行语义增强，然后使用一个可解释的度量标准对候选特征进行评分，该度量标准结合了类型兼容性、公差感知的尺寸一致性以及保守的上下文一致性，以及工程领域启发式方法。当确定性评分无法解决模糊性时，系统会升级到多模态和受约束的大语言模型推理，随后进行单个人工在环（HITL）审查步骤。在20对真实CAD绘图上的实验实现了平均精度83.67%、召回率90.46%和F1分数86.29%。消融研究显示，每个管道组件都对整体准确性有贡献，完整系统优于所有减少的变体。通过优先考虑确定性规则、清晰的决策跟踪以及保留未解决的案例供人工审查，该框架为现实工业环境中的下游制造自动化提供了实用的基础。
英文摘要：
Manufacturing automation in process planning, inspection planning, and digital-thread integration depends on a unified specification that binds the geometric features of a 3D CAD model to the geometric dimensioning and tolerancing (GD&T) callouts, datum definitions, and surface requirements carried by the corresponding 2D engineering drawing. Although Model-Based Definition (MBD) allows such specifications to be embedded directly in 3D models, 2D drawings remain the primary carrier of manufacturing intent in automotive, aerospace, shipbuilding, and heavy-machinery industries. Correctly linking drawing annotations to the corresponding 3D features is difficult because of contextual ambiguity, repeated feature patterns, and the need for transparent and traceable decisions. This paper presents a deterministic-first, context-aware framework that maps 2D drawing entities to 3D CAD features to produce a unified manufacturing specification. Drawing callouts are first semantically enriched and then scored against candidate features using an interpretable metric that combines type compatibility, tolerance-aware dimensional agreement, and conservative context consistency, along with engineering-domain heuristics. When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step. Experiments on 20 real CAD-drawing pairs achieve a mean precision of 83.67%, recall of 90.46%, and F1 score of 86.29%. An ablation study shows that each pipeline component contributes to overall accuracy, with the full system outperforming all reduced variants. By prioritizing deterministic rules, clear decision tracking, and retaining unresolved cases for human review, the framework provides a practical foundation for downstream manufacturing automation in real-world industrial environments.
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。
