The axle bridge plays a crucial role in the bogie of low-floor light rail vehicles, impacting operational efficiency and fuel economy. To minimize the total cost of the structure and turning of axle bridges, an optimization model of structural and turning parameters was built, with the fatigue life, maximum stress, maximum deformation, and maximum main cutting force as constraints. Through orthogonal experiments and multivariate variance analysis, the key design variables which have a significant impact on optimization objectives and constraints (performance responses) were identified. Then the optimal Latin hypercube design and finite element simulation was used to build a Radial Basis Function (RBF) model to approximate the implicit relationship between design variables and performance responses. Finally, a multi-island genetic algorithm was applied to solve the integrated optimization model, resulting in an 8.457% and 1.1% reduction in total cost compared with the original parameters and parameters of sequential optimization, proving the effectiveness of the proposed method.
