Parameter optimization of a hydraulic engine mount based on a genetic neural network

Abstract: Hydraulic engine mounts (HEMs) are important vehicle components to isolate the vehicle structure from engine vibration. A parameter optimization methodology for an HEM based on a genetic neural network (NN) model is proposed in this study. Samples of HEMs with different structures and rubber materials are manufactured and their dynamic characteristics are tested on an MTS 831 elastomer test system. Then the test results are used as samples to train the NN model which defines the non-linear global mapping relationship between the HEM’s structural parameters and its dynamic characteristics. The fitness values of the population in the genetic algorithm are calculated by the trained NN model, and the optimal solution was acquired with the mutation of population. Finally, experiments are made to validate the reliability of the optimal solution. The proposed optimization method can specify the structures and materials of HEMs to meet the design requirements automatically