🤖 AI Summary
Traditional engineering design relies heavily on costly simulations, while existing data-driven approaches often overlook the parametric nature and design semantics of CAD models, limiting their integration into design workflows and interpretability. This work proposes Attribute Feature Graphs (AFGs), which, for the first time, encode native CAD features—such as extrusions and ribs—as graph nodes, with directed edges representing geometric and dependency relationships. This representation preserves design intent while enabling end-to-end learning with graph neural networks (GNNs). Evaluated on the CarHoods10K dataset, the resulting GNN surrogate model achieves prediction accuracy comparable to state-of-the-art methods and supports direct feature editing within CAD environments with real-time performance feedback. Crucially, the approach provides traceability and interpretability by linking predictions back to specific design features.
📝 Abstract
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.