The escalating challenge of plastic waste necessitates innovative strategies that surpass conventional mechanical recycling.

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.

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

The growing global demand for sustainable energy has elevated the importance of energy-efficient electromechanical systems in applications such as electric vehicles (EVs), renewable energy conversion, and industrial automation. These systems reduce carbon emissions and operational costs by optimizing power use, aligning with UN Sustainable Development Goals and 2050 net-zero targets. However, traditional control methods like field-oriented control (FOC) and PID controllers struggle with nonlinear dynamics, parameter variations, and variable loads, resulting in suboptimal performance, higher energy losses, and reduced robustness. This paper addresses this gap by proposing an AI-assisted framework for designing and controlling smart electromechanical devices, using permanent magnet synchronous motor (PMSM) drives as a prototype. The approach integrates adaptive neural networks with reinforcement learning to enable real-time optimization of dynamic response, robustness, and energy efficiency. The system was rigorously simulated in MATLAB/Simulink using a d-q reference frame model under nominal, disturbed, and variable-load conditions. Results show significant improvements: transient settling time reduced by 42–58%, overshoot by 60–67%, and energy consumption by 12–18%, achieved through minimized torque ripple and losses. The framework also demonstrated superior disturbance rejection and parameter-variation stability. These advances position the proposed solution as a transformative approach for sustainable applications, enhancing efficiency in EV propulsion, renewable energy integration, and industrial automation, while paving the way for future hardware implementation and scalable AI-driven systems.