Mode Shape Identification Using Graph Neural Networks for Vehicle Structure Design RMIT University

Format:

Book

Conference/Event

Author/Creator:

Tohmuang, Sitthichart, author.

Contributor:

Bhide, Sanjay

Fard, Mohammad

Fayek, Haytham

Huber, John

Marzocca, P. (Piergiovanni)

Swayze, James L.

Conference Name:

Noise & Vibration Conference & Exhibition (2025-05-12 : Grand Rapids, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

This paper introduces a novel, automated approach for identifying and classifying full vehicle mode shapes using Graph Neural Networks (GNNs), a deep learning model for graph-structured data. Mode shape identification and naming refers to classifying deformation patterns in structures vibrating at natural frequencies with systematic naming based on the movement or deformation type. Many times, these mode shapes are named based on the type of movement or deformation involved. The systematic naming of mode shapes and their frequencies is essential for understanding structural dynamics and "Modal Alignment" or "Modal Separation" charts used in Noise, Vibration and Harshness (NVH) analysis. Current methods are manual, time-consuming, and rely on expert judgment. The integration of GNNs into mode shape classification represents a significant advancement in vehicle modal identification and structure design. Results demonstrate that GNNs offer superior accuracy and efficiency compared to current labor intensive manual modes labelling, making this innovative approach promising for vehicle NVH/dynamics analysis and structural design

Notes:

Vendor supplied data

Publisher Number:

2025-01-0131

Access Restriction:

Restricted for use by site license