A Deep Learning-Based CAE Approach for Simulating 3D Vehicle Wheels Under Real-World Conditions

Abstract

The implementation of deep learning (DL) in computer-aided engineering (CAE) can significantly improve the accuracy and efficiency of simulating 3D vehicle wheels under real-world conditions. While traditional CAE methods can be time-consuming and computationally expensive, DL can reduce simulation time and development cycles across all industries. This work explores the role of DL and AI in virtual manufacturing and CAE and investigates how they can be used to improve the accuracy and efficiency of simulations for 3D vehicle wheels. Deep learning models can learn the complex relationships between different wheel design parameters, such as tire load distribution, stress distribution, and fatigue life. Once trained, these models can be embedded into CAE software, allowing for faster and more accurate simulations of wheel performance. This interdisciplinary study uses various deep learning techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs), to create a more efficient and accurate relationship between CAD modeling and CAE simulation. The research aims to leverage the potential of deep learning models to automate 3D CAD design, accurately predict CAE results, and provide in-depth explanations and verifications. The benefits of this research are expected to extend to the automotive industry's pursuit of more robust and resilient wheel designs. By streamlining the product development process from conceptual design to engineering performance evaluation, this study has the potential to revolutionize the automotive industry's product development cycle.