AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan Ajou University

Format:

Book

Conference/Event

Author/Creator:

Lee, Jin Woo, author.

Cho, Jaeho, author.

Nam, Younsic, author.

Han, Yongha, author.

Conference Name:

14th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference (2026-06-17 : Graz, Austria)

Language:

English

Subjects (All):

Finite element analysis.

Optimization.

Neural networks.

Product development.

Acoustics.

Artificial intelligence (AI).

Local Subjects:

Finite element analysis.

Optimization.

Neural networks.

Product development.

Acoustics.

Artificial intelligence (AI).

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design stage, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which serve as training data, for various hole distributions. The trained model is then used to predict acoustic natural modes and natural frequencies from unseen input images representing different hole configurations in the package tray. These predictions are made in a fraction of the time required for traditional simulation methods, thereby validating the model's effectiveness. Furthermore, we demonstrate that the latent variables embedded in the trained model can be manipulated to control the acoustic modes and natural frequencies of the sedan. This indicates the potential for artificial intelligence-driven acoustic design optimization in early-stage vehicle development, offering both time efficiency and design flexibility without physical prototyping or extensive simulations

Notes:

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