Analysis and Prediction of Nonlinear Hysteretic Behaviors of Graphite Magnetorheological Grease at Different Temperatures

Abstract

Magnetorheological grease (MRG) is considered a promising alternative to magnetorheological fluids as a smart material because of its higher stability and less leakage. To enhance yield stresses in various applications, graphite is incorporated as an additive, resulting in graphite magnetorheological grease (GMRG). However, the nonlinear hysteresis properties of this new material and its prediction methods have not been investigated. Therefore, in this work, the nonlinear hysteretic properties of GMRG at different temperatures, magnetic fields, and frequencies are systematically investigated and compared with those of MRG. The results of rheological experiments show that graphite enhances the shear stress of GMRG in the hysteresis curve through its reinforcing effect on the magnetic chains. The strain hardening, elasticity, and viscosity of GMRG are enhanced, but an increase in temperature decreases this efficacy. A prediction model based on the particle swarm optimization neural network algorithm (PSO-BP) is also proposed to efficiently and accurately control the nonlinear behavior of GMRG in engineering. The hysteresis curves of GMRG under different external excitations are characterized and predicted by the particle swarm optimization neural network approach. The statistical results show that the PSO-BP-based hysteresis characteristic estimates have satisfactory accuracy. In the test data, the PSO-BP model demonstrates improvements in RMSE, MAE, and SMAPE by 19.5%, 20.6%, and 21.0%, respectively, compared to the conventional BP network, which maintains an R 2 value exceeding 0.95. This method provides an efficient solution for researchers to carry out reliable performance prediction in work such as genetic engineering of materials and related engineering applications.