article · Artificial Intelligence and Applications
Computer-aided engineering methods are essential for evaluating three-dimensional vehicle wheels, yet conventional simulations remain computationally demanding and slow. Integrating deep learning into computer-aided engineering offers a way to enhance both the precision and speed of wheel simulations under real-world conditions. Deep learning models can capture complex interactions among critical design variables, including tyre load distribution, mechanical stress, and fatigue life. Once trained, these systems can integrate directly into engineering software, accelerating performance assessments. By combining convolutional neural networks, generative adversarial networks, and recurrent neural networks, this approach bridges computer-aided design modelling and engineering simulations. The framework seeks to automate three-dimensional design workflows, predict performance outcomes reliably, and provide explanatory verification. Ultimately, this integration supports the automotive sector in developing more robust wheel structures while reducing overall product development timelines from initial concept to final validation.
Traditional vehicle engineering relies on slow, expensive digital simulations to ensure components withstand physical stress. Applying artificial intelligence to engineering simulations enables quicker testing of critical components like vehicle wheels. This accelerates the development of safer, more resilient automotive parts while lowering product development cycles for the automotive sector.
This work targets the automotive design and manufacturing sector, specifically engineering teams seeking faster prototyping and virtual testing of vehicle wheels. Embedding deep learning into computer-aided engineering software could automate three-dimensional design and performance prediction. Based on the abstract, the research represents an early-stage exploratory framework rather than an applied, tested product ready for market deployment.
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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. Received: 14 October 2023 | Revised: 8 December 2023 | Accepted: 4 January 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Timileyin Opeyemi Akande: Conceptualization, Methodology, Software. Oluwaseyi O. Alabi: Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Sunday A. Ajagbe: Validation, Formal analysis, Investigation, Resources, Funding acquisition.
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DOI: 10.47852/bonviewaia42021882
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