Adaptive Grid Optimization Algorithm for Mold Flow Analysis to Improve Computational Efficiency

This paper proposes an efficient mold flow analysis algorithm based on adaptive grid optimization, aiming to improve computational efficiency and ensure result accuracy. The strategy of dynamically adjusting grid density is adopted to achieve local fine division by identifying key areas of the flow domain, which significantly reduces the computational redundancy of unnecessary areas. In the mold flow simulation of the FANUC industrial robot teach pendant shell, the experimental results show that the calculation time is reduced by about 38% after optimization, the filling time prediction accuracy is improved to more than 98%, and the pressure distribution error is reduced to 0.5%. At the same time, the reliability of the algorithm in predicting flow characteristics and defect locations is verified by visual analysis of flow field traces and pressure cloud maps. Compared with traditional methods, the algorithm in this paper significantly reduces the uncertainty of injection defect prediction while improving computational efficiency, showing broad prospects in industrial applications of complex models.