Effects of iron particles’ volume fraction on compression mode properties of magnetorheological elastomers

Abstract

The effects of iron particle volume fraction (PVF) on the dynamic compression mode properties of isotropic and anisotropic magnetorheological elastomers (MREs) are experimentally investigated considering wide ranges of excitation frequency (1 Hz to 30 Hz), strain amplitude (2.5% to 20%) and magnetic flux density (0 to 750 mT). MRE samples with three different PVFs (15%, 30% and 45%) were fabricated in the laboratory and a test rig was designed to measure their magneto-mechanical characteristics. The effect of PVF on the loss modulus, denoted as PVF-dampening was substantially higher compared to that on the elastic modulus, denoted as PVF-stiffening. Moreover, PVF effects on compression mode properties of the isotropic MRE were greater compared to the anisotropic MRE, and showed coupled dependence on the excitation and magnetic field conditions. Results revealed that strain-rate stiffening of isotropic MRE increased with increasing PVF while slightly decreased for anisotropic MRE. Besides, strain-softening of both MREs increased with increasing PVF. Results were also suggesting of strong dependence of MR effect on the loading conditions, apart from the PVF. MR effect in view of elastic and loss moduli for isotropic MRE increased with increasing PVF from 15% to 45% at 1 Hz, but at higher frequency they become maximum around 30% PVF, regardless of strain amplitude. Both MR effects for anisotropic MRE, however, become minimum around 30% PVF, irrespective of frequency and strain amplitude. Owing to the observed coupled effects of the loading factors with the PVF, a simple phenomenological model was formulated considering independent functions in PVF, excitation frequency, strain amplitude and magnetic flux density for predicting compression elastic and loss moduli of both MREs. The proposed model required identifications of only six unknown constants, showed reasonably good agreements with the measured data.