Abstract

The increasing demand for customized industrial components challenges conventional design methods due to their time-consuming and iterative nature. Generative Artificial Intelligence (AI) models, including generative adversarial networks (GANs) and variational autoencoders (VAEs), offer a transformative approach to accelerate and optimize component design. This paper presents a framework leveraging generative AI to automatically produce feasible, high-performance designs for industrial parts based on functional requirements, material constraints, and manufacturing limitations. The system integrates parametric modeling, simulation-based validation, and AI-driven optimization to generate multiple candidate designs that meet performance criteria while minimizing weight, material usage, and production costs. Case studies on mechanical components such as gears, brackets, and housings demonstrate up to a 30% reduction in design iteration cycles and a 15% improvement in structural efficiency compared to conventional methods. The approach enables designers and engineers to explore a wider design space, supports rapid prototyping, and facilitates Industry 4.0 adoption through intelligent automation. Generative AI thus provides a scalable, data-driven solution for customized industrial component design, enhancing productivity, sustainability, and innovation in modern manufacturing environments.