earticle

영어

This study proposes a hierarchical optimization methodology for two-stage gear systems using Monte Carlo enhanced Genetic Algorithm (MCeGA). The approach integrates reliability-based design with genetic algorithms to overcome the inherent randomness of traditional GA methods. A two-phase optimization framework was developed. The system incorporates Unity engine for real-time 3D visualization and interactive design evaluation. Key design constraints including contact ratio, gear ratio, and meshing conditions were parameterized according to ISO 6336 and AGMA 2101 standards. The proposed framework enables application-specific optimal gear configurations through Pareto analysis and weighted optimization, providing engineers with practical design solutions for various industrial requirements.