Experiments and modeling of mechanically-soft, hard magnetorheological foams with potential applications in haptic sensing
Zehui Lin, Zahra Hooshmand-Ahoor, Laurence Bodelot, Kostas Danas
TL;DR
This work tackles sensing deformation in mechanically-soft, magnetically-hard h-MRE foams by leveraging deformation-induced changes in external magnetic flux without requiring continuous external fields. It develops a thermodynamically consistent, compressible magneto-elastic framework that couples finite-strain mechanics with magnetism via an internal remanent field, using a two-scale homogenization to express mechanical and magnetic energies in terms of porosity and particle content. The model, calibrated against oedometric data and supported by FE simulations and analytical Gou-based solutions, accurately predicts both mechanical and magnetic responses across multiple particle fractions and loading conditions, enabling inference of deformation, stiffness, and stress from magnetic signals. The results demonstrate the feasibility of multi-modal haptic sensing with 3D-printed-like isotropic foams, offering a path toward passive, embedded magnetic sensing in soft actuators and tactile devices, with future work focusing on dissipative dynamics and broader loading regimes.
Abstract
This study proposes a family of novel mechanically-soft and magnetically-hard magnetorheological foams that, upon deformation, lead to robust and measurable magnetic flux changes in their surroundings. This allows to infer qualitatively and even quantitatively the imposed deformation and, eventually from that, an estimation of the stiffness and average stress on the sample even in complex loading scenarios involving combinations of uniform or nonuniform compression/tension with superposed shearing in different directions. The work provides a complete experimental, theoretical and numerical framework on finite strain, compressible magneto-elasticity, thereby allowing to measure and predict coupled magneto-mechanical properties of such materials with different particle volume fractions and then use it to estimate and design potential haptic sensing devices.
