  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impact tests have been replaced by computer simulations such as finite element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.
