用大模型辅助推理，精准匹配2D图纸标注与3D模型特征。
Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation
- 先用确定性规则匹配，再用大模型处理模糊情况。
- 在20组真实数据上达到F1 86.29%，精度83.67%、召回90.46%。
- 适合制造业自动化、数字主线集成等工业场景使用。
制造自动化中的工艺规划、检验规划和数字主线集成依赖于将3D CAD模型的几何特征与对应的2D工程图中的几何尺寸公差（GD&T）标注、基准定义及表面要求统一关联。尽管基于模型的定义（MBD）可将此类规范直接嵌入3D模型，但在汽车、航空航天、造船和重型机械行业中，2D图纸仍是制造意图的主要载体。由于上下文歧义、重复特征模式以及对透明可追溯决策的需求，正确关联图纸标注与3D特征仍具挑战。本文提出一种确定性优先、上下文感知的框架，将2D图纸实体映射到3D CAD特征，生成统一的制造规范。首先对图纸标注进行语义增强，然后通过结合类型兼容性、容差感知的尺寸一致性及保守上下文一致性的可解释度量，对候选特征进行评分，并融入工程领域启发式规则。当确定性评分无法解决歧义时，系统升级至多模态且受约束的大语言模型推理，并最终通过单次人工在环（HITL）审查确认。在20组真实CAD-图纸配对上，平均精确率为83.67%，召回率为90.46%，F1得分为86.29%。消融实验表明，每个模块均对整体准确率有贡献，完整系统优于所有简化变体。通过优先使用确定性规则、明确决策追踪并保留未决案例供人工审核，该框架为现实工业环境中的下游制造自动化提供了实用基础。
原文摘要 · Abstract (English)
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.
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