Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

For ensuring vehicle safety, the impact performance of wheels during wheel development must be ensured through a wheel impact test. However, manufacturing and testing a real wheel requires a significant time and money because developing an optimal wheel design requires numerous iterative processes t...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Shin, Seungyeon, Ah-hyeon Jin, Yoo, Soyoung, Lee, Sunghee, Kim, ChangGon, Heo, Sungpil, Kang, Namwoo
Format: Article
Language:English
Subjects:
Aluminum
Cost analysis
Deep learning
Finite element method
Impact prediction
Impact tests
Iterative methods
Performance prediction
Prediction models
Stress distribution
Vehicle safety
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For ensuring vehicle safety, the impact performance of wheels during wheel development must be ensured through a wheel impact test. However, manufacturing and testing a real wheel requires a significant time and money because developing an optimal wheel design requires numerous iterative processes to modify the wheel design and verify the safety performance. Accordingly, wheel impact tests have been replaced by computer simulations such as finite element analysis (FEA); however, it still incurs high computational costs for modeling and analysis, and requires FEA experts. In this study, we present an aluminum road wheel impact performance prediction model based on deep learning that replaces computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass values used for the wheel impact test were utilized as the inputs to predict the magnitude of the maximum von Mises stress, corresponding location, and the stress distribution of the 2D disk-view. The input data were first compressed into a latent space with a 3D convolutional variational autoencoder (cVAE) and 2D convolutional autoencoder (cAE). Subsequently, the fully connected layers were used to predict the impact performance, and a decoder was used to predict the stress distribution heatmap of the 2D disk-view. The proposed model can replace the impact test in the early wheel-development stage by predicting the impact performance in real-time and can be used without domain knowledge. The time required for the wheel development process can be reduced by using this mechanism.
ISSN:2331-8422