Multi-Objective Design of Heat Sink Fins for Thermal Efficiency and Manufacturability

As power densities in modern electronics increase, efficient thermal management is essential. Conventional heat sink designs often fail to balance heat dissipation, airflow resistance, and manufacturability. This study proposes an AI-driven optimization framework, integrating deep reinforcement learning (DRL) and multi-objective genetic algorithms (MOGA), to refine fin geometries while ensuring fabrication feasibility. Unlike conventional methods, this approach incorporates additive manufacturing constraints, bridging the gap between computational optimization and real-world implementation. Validated through computational fluid dynamics (CFD) simulations and experimental fabrication, the optimized design achieved a 14.3% reduction in maximum temperature and a 32.8% decrease in thermal resistance, ensuring a more uniform temperature distribution. It also maintained stable cooling performance across airflow variations, confirming its adaptability. Manufacturability analysis revealed height deviations of up to 0.4 mm, which could affect airflow, while thickness deviations remained within ± 0.05 mm, indicating high precision. These results highlight the importance of integrating fabrication constraints early in the design process to ensure optimization benefits translate into practical performance. This study shows that AI-driven optimization can enhance heat sink efficiency and reliability, offering a scalable approach for high-power electronics. Future work should refine manufacturing compensation models and transient thermal analysis to further improve real-world applicability.