[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81823-en":3,"doc-seo-81823-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81823,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks","Body-in-White (BiW) mode shape recognition is essential in automotive NVH development, but current solutions rely on manual visual inspection or engineering heuristics, MAC, and geometry-dependent AI features that lack robustness across different vehicle architectures, FE meshes, and experimental measurement layouts. This paper proposes a canonical engineering graph representation and region-aware graph learning framework that unifies heterogeneous FE models and experiments into semantic structural-region nodes. Region descriptors and attention with region-aware pooling enable transferable, explainable predictions under severe label scarcity across multiple vehicle programs, decoupling engineering knowledge from discretization.","arXiv :2607 .01522v1 [ ee ss . SY] 1 Jul 2026  \nRobust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks  \nTong Duy Son 1 , Marc Brughmans 1 , Andrey Hense 1,2 , Kohta Sugiura 3 , Sebastian Ciceo 1 , Paolo di Carlo 1 , Theo Geluk 1  \n1 Siemens Digital Industries Software, Interleuvenlaan 68, 3001 Leuven, Belgium  \ne-mail: [son.tong@siemens.com](son.tong@siemens.com)  \n2 KU Leuven, Campus Diepenbeek, Department of Mechanical Engineering Wetenschapspark 27, B-3590, Diepenbeek, Belgium  \n3 Siemens Digital Industries Software, 3-1-9 Shin-Yokohama, Yokohama 222-0033, Japan  \nAbstract  \nBody-in-White (BiW) mode shape recognition is a fundamental task in automotive noise, vibration, and harshness (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 applicability in industrial development.  \nThis 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 learning to transfer across different vehicle programs without requiring identical mesh topology or sensor configurations.  \nThe proposed framework is validated using FE and experimental datasets from four vehicle programs under severe label scarcity. Experimental results demonstrate high classification accuracy, cross-vehicle transferability, and physically meaningful explanations by directly relating model predictions to engineering-defined structural regions used in NVH analysis. Beyond mode shape recognition, the proposed Canonical Engineering Graph Representation establishes a reusable engineering abstraction that can support trustworthy and transferable AI across heterogeneous simulation and experimental workflows.  \n1 Introduction  \nAutomotive product development relies heavily on simulation-driven engineering, where computer-aided engineering (CAE) and computational fluid dynamics (CFD) are used to evaluate structural, vibro-acoustic, and aerodynamic performance before physical prototypes are finalized. In structural development, finite element (FE) models are widely used to analyze body stiffness, modal behaviour, and noise, vibration, and harshness  \nFull Vehicle  \nTrim Body  \nFE Body-in-White  \nTest Body Data  \nClassification: Mode Shape Recognition  \n(torsion, bending, pumping, local modes   )  \n✓ From single dataset to large scale variations  \n✓ Fast, Scalable  \nTransferable  \nAcross simulation variants and test data  \nGeneralizable  \nUsable across historical data and workflows  \nExplainable  \nResults reflect engineering intuition  \nData Generation  \nGuide additional  \nsimulations and labels  \nFigure 1: Overview of the proposed Canonical Engineering Graph Representation and region-aware graph learning framework for explainable and transferable Body-in-White mode shape classification.  \n(NVH), while aerodynamic simulations are used to predict drag, pressure distribution, wall shear stress, and flow behaviour around the vehicle [1, 2] . These analyses play a central role in engineering decisions related to body design, structur","cbCaivTeTws7e5to","https://ap.wps.com/l/cbCaivTeTws7e5to","pdf",8306292,6,1,17,"English","en",105,"# Abstract\n# Introduction\n# Overview of the Proposed Framework","[{\"question\":\"Why is BiW mode shape recognition challenging in industrial automotive NVH development?\",\"answer\":\"It often depends on manual visual inspection by experienced engineers, while many existing methods based on heuristics, MAC, or geometry-dependent AI representations struggle to remain robust across vehicle programs, FE meshes, and experimental layouts.\"},{\"question\":\"What is the core idea of the proposed region-aware graph learning framework?\",\"answer\":\"Heterogeneous FE models and experimental measurements are transformed into a common graph where nodes correspond to semantically meaningful structural regions connected through engineering-informed relationships.\"},{\"question\":\"How does the method achieve robustness and explainability across different vehicle programs?\",\"answer\":\"Geometry-independent regional descriptors combined with graph attention learning and region-aware pooling preserve engineering semantics, allowing knowledge transfer without requiring identical mesh topology or sensor configurations, and producing explanations tied to engineering-defined regions used in NVH analysis.\"}]","Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks | 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is BiW mode shape recognition challenging in industrial automotive NVH development?","Question",{"text":77,"@type":78},"It often depends on manual visual inspection by experienced engineers, while many existing methods based on heuristics, MAC, or geometry-dependent AI representations struggle to remain robust across vehicle programs, FE meshes, and experimental layouts.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the core idea of the proposed region-aware graph learning framework?",{"text":82,"@type":78},"Heterogeneous FE models and experimental measurements are transformed into a common graph where nodes correspond to semantically meaningful structural regions connected through engineering-informed relationships.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the method achieve robustness and explainability across different vehicle programs?",{"text":86,"@type":78},"Geometry-independent regional descriptors combined with graph attention learning and 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