A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

Abstract

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 Use of AI to Analyze Social Media Attacks for Predictive Analytics

“…SEAD determines SEA's "integrity" across all social media post by averaging the threat factor across three detection components when doing risk analysis [3], [26]- [28]. Based on heuristics, each individual component is initially graded on a scale from 0 to 1.…”

“…Furthermore, hackers can now simply track the activity of real users on social media networks by making straightforward Application Programming Interface API requests. Reconnaissance is a common first step in SE assaults [3]. Before initiating vicious attacks that sound plausible to the victims, the attacker spends much time researching user behaviors, such as their preferred products and routines [4], [5].…”