Application Study of Nonlinear Viscoelastic Constitutive Model for Dynamic Behavior of Suspension Arm Bushing

Paper #:
  • 2016-01-1375

Published:
  • 2016-04-05
DOI:
  • 10.4271/2016-01-1375
Citation:
Ueda, M., Ito, S., and Suzuki, D., "Application Study of Nonlinear Viscoelastic Constitutive Model for Dynamic Behavior of Suspension Arm Bushing," SAE Int. J. Passeng. Cars - Mech. Syst. 9(1):336-345, 2016, doi:10.4271/2016-01-1375.
Pages:
10
Abstract:
Ride quality is an important purchasing consideration for consumers. It is typically defined in terms of noise, vibration and harshness. These phenomena are a result of vibrations caused at the engine/powertrain and from the road surface, which are transmitted to the passenger cabin. To minimize such vibrations, rubber parts are used extensively at mounting points for the cabin, such as engine mountings and suspension bushings. The vehicle development process increasingly requires performance testing, including rubber parts using CAE, prior to prototype evaluation. This in turn requires a rubber material model that can accurately describe dynamic characteristics of rubber components, particularly frequency and amplitude dependency. Conventional rubber models using commercially available structural analysis solvers cannot solve for both frequency and amplitude dependency at the same time, and are unable to predict transient phenomena such as harshness that involve inputs of varying amplitude. The authors have proposed a new rubber material model that is able to reproduce both frequency and amplitude dependency simultaneously, based on the rubber material model developed by Simo, J.C. [1]. Previous studies have demonstrated the accuracy of the new model under quasi-static and harmonic input conditions. Actual vehicle evaluation involves several input directions, with simultaneous translational and rotational inputs that are transient. In this paper, the new rubber material model is applied to a suspension arm bushing to confirm bushing force when subjected to complex inputs. The model was shown to predict bushing stiffness with greater accuracy and therefore was validated.
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