Parameter Identification of the Mooney–Rivlin Model for Rubber Mounts Subject to Multiaxial Load

• Published in: Journal of Vibration Engineering & Technologies Vol. 13; no. 1
• Main Authors: Yan, Jing, Zhang, Zaicheng, Man, Jianhao, Sun, Jiawei, Zhen, Ran, Liu, Xiao-ang
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.01.2025
• Subjects: Acoustics; Control; Dynamical Systems; Engineering; Engineering Acoustics; Original Paper; Vibration; Mooney–Rivlin model; Rubber mount; Bayesian inference; Parameters identification
• ISSN: 2523-3920; 2523-3939
• DOI: 10.1007/s42417-024-01594-8

Purpose To improve the simulation accuracy of the static characteristics of rubber under multiaxial loading, this study proposed a method to determine the parameters of the constitutive model of rubber under such conditions. Methods Initially, a multiaxial loading test platform was developed to verify the effect of multiaxial loading on the static properties of rubber. Subsequently, considering both the interval uncertainty of the applied loads and the stochastic uncertainty of measurement noise, a new method combining Bayesian inference, interval analysis, and response surface modeling was used to develop a Mooney-Rivlin constitutive parameter identification model. The constitutive parameters C 01 and C 10 were successfully predicted using this method, validated using finite element simulations and experiments, and the errors were calculated. Finally, the effects of multiaxial loading (Z-direction preloading and X/Y-direction working loading) and measurement noise on the identification process were discussed. Results The results indicated that the static stiffness rises in all axial directions as the preload level increased. The intrinsic parameters identified by considering preload ensured that the error in the experimental and simulated stiffness values remained within 15% in all directions compared to the case without considering preload. It was found that considering preload significantly improves the accuracy of static stiffness prediction.