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.