Engineering design is an iterative, simulation-driven process where traditional workflows rely heavily on computationally expensive analyses such as finite element and computational fluid dynamics. Although data-driven methods have accelerated design evaluation and optimization, most existing geometric representations discard parametric and feature-level semantics, limiting their integration with CAD-driven design workflows and reducing model interpretability. To address this gap, this work introduces Attributed Feature Graphs (AFGs), a feature-based representation that encodes design features, such as extrusions, ribs, and pockets, as nodes and their geometric or dependency relations as directed edges. AFGs preserve design intent and parametric structure while remaining compatible with standard graph-based learning methods, enabling end-to-end learning directly on CAD-derived feature graphs. The paper demonstrates the proposed representation through a surrogate-modeling case study on the CarHoods10K automotive hood frame dataset, where a Graph Neural Network (GNN) is trained as an evaluation engine to predict performance metrics from AFG inputs. The learned model achieves competitive surrogate performance compared with traditional data-driven approaches, but with the added benefit that engineers can map predictions back to specific CAD features and interpret how individual design elements influence system behavior. Furthermore, because AFGs are built from native CAD features, engineers can directly edit the underlying geometry in the CAD environment and reevaluate the design through the same learned model.

翻译：工程设计是一个迭代的、仿真驱动的过程，其传统工作流程高度依赖有限元分析和计算流体动力学等高计算成本的分析方法。尽管数据驱动方法加速了设计评估与优化，但现有几何表示大多摒弃了参数化和特征级语义，这限制了它们与CAD驱动设计流程的集成，并降低了模型可解释性。为填补这一空白，本文提出归因特征图——一种基于特征的表示方法，将拉伸、肋板和凹槽等设计特征编码为节点，将其几何或依赖关系编码为有向边。AFG保留了设计意图和参数结构，同时兼容标准图学习方法，可直接在基于CAD的特征图上进行端到端学习。本文通过CarHoods10K汽车发动机罩框架数据集的替代模型案例研究展示了所提出的表示方法，其中使用图神经网络训练评估引擎，从AFG输入预测性能指标。与传统数据驱动方法相比，该学习模型在替代性能上具有竞争力，且附加优势在于工程师可将预测结果映射至具体CAD特征，并解释单个设计元素如何影响系统行为。此外，由于AFG源自原生CAD特征，工程师可直接在CAD环境中编辑底层几何结构，并通过同一学习模型重新评估设计。