AI-Driven Multi-Objective Optimization of Heat Sink Design for Enhanced Heat Dissipation Efficiency and Manufacturability

This study presents a novel AI-driven multiobjective optimization framework that integrates a PhysicsInformed Generative Adversarial Network (PI-GAN) with a Multi-Objective Genetic Algorithm (MOGA) for the innovative optimization of heat sink geometries. The proposed framework initially employs PI-GAN under strict physical constraints to generate unconventional and innovative heat sink designs, thereby overcoming the limitations of traditional approaches. Subsequently, MOGA is utilized to conduct a global search under multi-objective conditions by simultaneously considering several performance indices-including thermal performance, airflow resistance, and manufacturability-and optimizing key geometric parameters (such as spacing, thickness, and height) through Computational Fluid Dynamics (CFD) simulation analysis. As a result, the optimized design achieves significant enhancements: $16.8 \%$ reduction in maximum temperature, $25.5 \%$ reduction in airflow resistance, and $35.8 \%$ decrease in thermal resistance, while effectively compensating for manufacturing deviations to ensure feasibility in both additive manufacturing and die-casting processes. The results indicate that the integration of PI-GAN with MOGA not only transcends the limitations of conventional geometric design by enabling more innovative heat sink structures, but also maintains stable thermal management performance under varying wind speed conditions. Overall, this study offers an innovative yet practical solution for the design of thermal management systems in highperformance electronic devices, demonstrating the broad application potential of generative adversarial networks and multi-objective optimization methods in engineering design.