Abstract
This study aims to improve the structural strength and reduce the weight of a full-suspension electric-assisted bike frame using a multi-objective optimization approach. A comprehensive and major design framework is implemented, integrating the uniform design of experiments, Kriging interpolation, entropy weight method, technique for order preference by similarity to an ideal solution (TOPSIS), and a genetic algorithm. Five geometric parameters of the bike frame are selected as control factors in the uniform design. Finite element analysis is conducted to simulate impact displacements under the EN 15194 standard test conditions. Each simulation evaluates frame performance across various parameter combinations. The proposed optimization strategy is validated through falling-mass and falling-frame impact simulations, achieving respective improvements of 4.53% and 5.29% over the original design. Furthermore, the optimized frame demonstrates a weight reduction of 52.51 g. A full factorial design and analysis of variance are employed to identify the most influential parameters, and a comprehensive sensitivity analysis evaluates the relative impact of each factor on the objective functions. These results confirm the effectiveness of the proposed optimization method in producing a stronger and lighter bike frame.
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