🤖 AI Summary
This study addresses the limited robustness and poor engineering applicability of traditional modal shape identification methods, which heavily rely on manual expertise and struggle with variations in vehicle models, finite element meshes, and sensor layouts. To overcome these challenges, this work proposes an engineering semantics–driven graph neural network framework that constructs a geometry-agnostic canonical engineering graph representation, thereby unifying heterogeneous finite element models and experimental data while decoupling engineering knowledge from numerical discretization to enable cross-vehicle transferability. The approach incorporates a region-aware graph attention mechanism, engineering relation–guided graph pooling, and geometry-invariant regional descriptors to achieve physically interpretable 3D modal shape recognition. Validation across four vehicle datasets demonstrates high classification accuracy and strong generalization across vehicle types—even under label scarcity—with identified regions showing close alignment with established NVH engineering zones.
📝 Abstract
Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engineers. Existing approaches based on engineering heuristics, Modal Assurance Criterion (MAC), or geometry-dependent AI representations often exhibit limited robustness across different vehicle architectures, finite element (FE) meshes, and experimental measurement layouts, restricting their industrial applicability. This paper presents a Canonical Engineering Graph Representation and region-aware graph learning framework for robust and explainable 3D mode shape recognition. Rather than learning directly from vehicle-specific FE meshes, heterogeneous FE models and experimental measurements are transformed into a common graph whose nodes represent semantically meaningful structural regions connected through engineering-informed relationships. Geometry-independent regional descriptors are combined with graph attention learning and region-aware pooling to capture structural interactions while preserving engineering semantics and enabling physically interpretable predictions. The resulting representation decouples engineering knowledge from numerical discretization, allowing transfer across different vehicle programs without requiring identical mesh topology or sensor configurations. The proposed framework is validated using FE and experimental datasets from four vehicle programs under severe label scarcity. Results demonstrate high classification accuracy, cross-vehicle transferability, and physically meaningful explanations by directly relating predictions to engineering-defined structural regions used in NVH analysis. Beyond mode shape recognition, the proposed Canonical Engineering Graph Representation provides a reusable engineering abstraction for trustworthy and transferable AI across heterogeneous simulation and experimental workflows.