Intelligent design of cooling systems for aluminum alloy die-casting dies: A framework integrating topology optimization and particle swarm optimization

With the growing demand for lightweight and high-performance components in automotive and aerospace industries, aluminum alloy die-castings are evolving toward larger dimensions and thinner walls, posing significant challenges to thermal management during solidification. Traditional cooling channel designs often fail to ensure uniform temperature distribution, leading to defects such as shrinkage porosity and deformation. This study proposes an automated design framework integrating the moving morphable components (MMC) topology optimization method with particle swarm optimization (PSO) to generate efficient and manufacturable cooling channel layouts for A380 aluminum alloys. Firstly, a systematic initialization strategy was developed with component dimensions of 4–10 mm in width and 15–40 mm in length, along with discrete orientation angles. The optimization process effectively guided components toward high-temperature regions identified through numerical simulation, followed by post-processing operations including temperature-based sorting, overlap removal, and component interconnection. The final design with 20 retained components was selected. Then, castings with a conventional cooling system and without any cooling system were employed as benchmark cases for comparison with the current optimized design. Compared with the conventional and no-cooling cases, the current cooling system exhibits a consistently lower temperature standard deviation after 30 s, maintains superior thermal uniformity throughout solidification, and achieves this improvement without comprising the average temperature.