摘要: Two-dimensional patterned hollow structures (2D-PHS) have emerged as advanced materials with exceptional mechanical properties and lightweight characteristics, making them ideal for high-performance applications in aerospace and automotive industries. However, optimizing their structural design to achieve uniform stress distribution and minimize stress concentrations remains a significant challenge due to the complex interplay between geometric patterns and mechanical performance. In this study, we develop an integrated framework combining Conditional Generative Adversarial Networks (cGAN) and Deep Q-Network (DQN) to predict and optimize the stress fields of 2D-PHS. We generated a comprehensive dataset comprising 1,000 samples across five distinct density classes using a custom grid pattern generation algorithm, ensuring a wide range of structural variations. The cGAN accurately predicts stress distributions, achieving a high correlation with finite element analysis (FEA) results while reducing computational time from approximately 40 seconds (FEA) to just 1-2 seconds per prediction. Concurrently, the DQN optimizes design parameters through scaling and rotation operations, enhancing structural performance based on predicted stress metrics. Our approach resulted in a 4.3% improvement in average stress uniformity and a 23.1% reduction in maximum stress concentrations. These improvements were validated through FEA simulations and experimental tensile tests on 3D printed TPU samples. The tensile strength of optimized samples increased from an initial average of 5.9 MPa to 6.6 MPa under 100% strain, demonstrating enhanced mechanical resilience. This study demonstrates the efficacy of combining advanced AI techniques for rapid and precise material design optimization, providing a scalable and cost-effective solution for developing superior lightweight materials with tailored mechanical properties for critical engineering applications.