A physics-guided deep learning model for predicting the magneto-induced mechanical properties of magnetorheological elastomer: Small experimental data-driven

Abstract

Magnetorheological elastomer (MRE) is a novel intelligent material, which shows excellent potential in vibration control applications. Previous researches have fully demonstrated that the magneto-induced shear storage modulus of MRE largely determines the vibration control effect. However, both existing theoretical and experimental ways to measure the magneto-induced shear storage modulus of MRE face their own shortage. Therefore, a novel physics-guided deep learning model is proposed to efficient predict the magneto-induced mechanical properties of MRE based on Magnetic Dipole theory and data-driven methods. A small database is built by collecting the magneto-induced shear storage modulus of MRE with different material ratios tested on a special shear rheometer. The proposed model trained with small training samples and its prediction results fit well with experimental values (average R2 of 0.99) which is superior to existing constitutive models. The training only takes 25 s, which significantly shortens the time compared to the experiment. Furthermore, the proposed model effectively predicts the magneto-induced storage modulus of MRE and has good generalization and superior transfer performance.