Research on Product Design Automation and Optimization Based on Deep Learning

The integration of deep learning into product design automation and optimization represents a transformative step in modern design methodologies, aligning with advances in computational frameworks explored in the field of computer science. Traditional approaches to product design, characterized by static methods and limited adaptability, struggle to address the high-dimensional and multi-objective nature of contemporary design challenges. This study introduces the Generative Design Optimization Model (GDOM), a novel framework leveraging generative models such as variational autoencoders and reinforcement learning to explore and optimize complex design spaces efficiently. Complemented by the Design-Adaptive Optimization Strategy (DAOS), the framework dynamically adjusts objectives and constraints, integrates real-time feedback, and enhances exploration through adaptive reinforcement mechanisms. The integration of surrogate modeling and latent space optimization further reduces computational overhead while maintaining solution fidelity. Experimental validation demonstrates that the proposed methods achieve significant improvements in design efficiency, functional performance, and user-centric adaptability compared to conventional techniques. This research highlights the potential of deep learning in revolutionizing product design, offering scalable and robust solutions to meet diverse and evolving design requirements.