Dynamic Performance Optimization of Double-isolation rubber mounts Based on Neural Network and Genetic Algorithm Collaboration

As a crucial connecting component between the powertrain and the chassis, the performance of rubber mounts is directly related to the NVH (Noise, Vibration, and Harshness) characteristics of electric vehicles. This paper proposes a double-isolation rubber mount, which, compared to traditional rubber mounts, incorporates an intermediate skeleton and features inner and outer layers of "cross-ribs". The design parameters can be simplified to: skeleton diameter, skeleton thickness, main rib width, and main rib thickness. To comprehensively evaluate its performance, a finite element analysis (FEA) model of the proposed double-isolation rubber mount was first established in Abaqus, with static stiffness and dynamic performance analyzed separately. The results indicate that, compared to traditional rubber mounts with similar static stiffness, this design effectively controls dynamic stiffness in the high-frequency range. To expand the effective vibration isolation frequency range of the double-isolation rubber mount, an L25(5^5) orthogonal experimental design was constructed based on the four structural parameters to assess the impact of each parameter on performance.Using static stiffness and dynamic stiffness peak frequency as optimization objectives, and the four structural parameters as design variables, a neural network-assisted genetic algorithm was employed for dynamic performance optimization of the rubber mount. Finally, based on the obtained optimal design parameters, the dynamic performance of the double-isolation rubber mount was tested. The analysis shows that the test results align with the computational results, demonstrating the practicality of the proposed double-isolation rubber mount.