1 research outputs found
Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress
The impact performance of the wheel during wheel development must be ensured
through a wheel impact test for vehicle safety. However, manufacturing and
testing a real wheel take a significant amount of time and money because
developing an optimal wheel design requires numerous iterative processes of
modifying the wheel design and verifying the safety performance. Accordingly,
the actual wheel impact test has been replaced by computer simulations, such as
Finite Element Analysis (FEA), but it still requires high computational costs
for modeling and analysis. Moreover, FEA experts are needed. This study
presents an aluminum road wheel impact performance prediction model based on
deep learning that replaces the computationally expensive and time-consuming 3D
FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and
barrier mass value used for wheel impact test are utilized as the inputs to
predict the magnitude of maximum von Mises stress, corresponding location, and
the stress distribution of 2D disk-view. The wheel impact performance
prediction 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
shortened through this mechanism