Parameters Identification of Mooney-Rivlin Model for Rubber Mount Based on Surrogate Model

As an important vibration damping element in automobile, the rubber mount can effectively reduce the vibration transmitted from the engine to the frame. In this study, a method of parameters identification of Mooney-Rivlin model by using surrogate model was proposed to more accurately describe the mechanical behavior of mount. Firstly, taking the rubber mount as the research object, the stiffness measurement was carried out. And then the calculation model of the rubber mount was established with Mooney-Rivlin model. Latin hypercube sampling was used to obtain the force and displacement calculation data in different directions. Then, the parameters of the Mooney-Rivlin model were taken as the design variables. And the error of the measured force-displacement curve and the calculated force-displacement curve was taken as the system response. Two surrogate models, the response surface model and the back-propagation neural network, were established. In addition, their prediction accuracy was compared and analyzed. For the prediction accuracy, the response surface model is more accurate than the back-propagation neural network. Finally, the surrogate model was combined with crow search algorithm to obtain the minimum error between the measured force-displacement curve and the calculated force-displacement curve. And the parameters of the Mooney-Rivlin model were identified with the presented method. The results show that the relative errors between the calculated stiffness and the measured stiffness in the three directions are less than 3%, which proving the identified parameters are accurate.