Generative AI-Assisted Engineering Design Optimization for Sustainable Manufacturing

Abstract

Sustainable manufacturing increasingly relies on Generative AI to optimize engineering design while reducing environmental impact, resource consumption, and production costs. By integrating deep learning, large language models, diffusion models, reinforcement learning, and digital engineering, Generative AI enables autonomous design generation, multi-objective optimization, and lifecycle sustainability assessment. The proposed framework enhances material efficiency, energy savings, carbon reduction, and manufacturing flexibility while accelerating product development. Despite challenges such as explainability, computational complexity, and design validation, Generative AI offers significant potential to transform sustainable manufacturing through intelligent, environmentally responsible, and data-driven engineering design.