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Acta Mechanica

, Volume 210, Issue 3–4, pp 183–214 | Cite as

Transversely isotropic nonlinear magneto-active elastomers

  • Roger BustamanteEmail author
Article

Abstract

Magneto-active elastomers are smart materials composed of a rubber-like matrix material containing a distribution of magneto active particles. The large elastic deformations possible in the rubber-like matrix allow the mechanical properties of magneto-active elastomers to be changed significantly by the application of external magnetic fields. In this paper, we provide a theoretical basis for the description of the nonlinear properties of a particular class of these materials, namely transversely isotropic magneto-active elastomers. The transversely isotropic character of these materials is produced by the application of a magnetic field during the curing process, when the magneto active particles are distributed within the rubber. As a result the particles are aligned in chains that generated a preferred direction in the material. Available experimental data suggest that this enhances the stiffness of the material in the presence of an external magnetic field by comparison with the situation in which no external field is applied during curing, which leads to an essentially random (isotropic) distribution of particles. Herein, we develop a general form of the constitutive law for such magnetoelastic solids. This is then used in the solution of two simple problems involving homogeneous deformations, namely simple shear of a slab and simple tension of a cylinder. Using these results and the experimental available data we develop a prototype constitutive equation, which is used in order to solve two boundary-value problems involving non-homogeneous deformations—the extension and inflation of a circular cylindrical tube and the extension and torsion of a solid circular cylinder.

Keywords

Energy Function Isotropic Material Simple Shear Isotropic Case Maxwell Stress 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Departamento de Ingeniería MecánicaUniversidad de ChileSantiagoChile

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