Considering persistent years, many researchers continuously seek an optimum way to utilize the idea of magnetorheology (MR) materials to be practically used for everyday life, particularly concerning resistivity sensing application. The rheology and resistivity of a graphite (Gr)-based magnetorheological elastomer (Gr-MRE) were experimentally evaluated in the present research. Magnetorheological elastomer (MRE) samples were prepared by adding Gr as a new additive during MRE fabrication. The effect of additional Gr on the rheological and resistivity properties were investigated and compared with those of typical MREs without a Gr additive. Morphological aspects of Gr-MRE were characterized using field emission scanning electron microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDX). Rheological properties under different magnetic fields were evaluated using a parallel-plate rheometer. Subsequently, the resistivity of all samples was measured under different applied forces and magnetic fields. From the resistivity evaluation, two relationship curves resistance (R) under different applied forces (F) and different magnetic fields (B) were established and plotted by using an empirical model. It was observed from the FESEM images that the presence of Gr fractions arrangement contributes to the conductivity of MRE. It was also observed that, with the addition of Gr, rheological properties such as the field-dependent modulus can be improved, particularly at low strain amplitudes. It is also demonstrated that the addition of Gr in MRE can contribute to the likely use of force detection in tactile sensing devices.

This work presents constitutive models of magnetorheological (MR) fluids, which can predict the shear and dynamic yield stress depending on temperature. Two existing models, the Herschel–Bulkley rheological and power law model, which are frequently used in MR fluid research, are adopted and modified to take the temperature into account. A new constitutive model of MR fluids is developed using the extreme learning machine (ELM) method. In this development, among many machine learning approaches, a simple and efficient learning algorithm for a single hidden layer feed-forward neural network (SLFN) is adopted and applied to the rheological model of MR fluids. The temperature, shear rate, and magnetic field are treated as inputs, and the shear stress is taken as an output. After formulating the models associated with experimental coefficients, the two most important properties of MR fluids; the shear and yield stress are predicted and compared with the measured values. The prediction accuracy for the field-dependent rheological properties of MR fluids in several different temperatures is evaluated and compared. It is shown that the ELM model developed in this work provides the best accuracy, followed by two other modified constitutive equations.

This paper investigates the field-dependent rheological properties of magnetorheological (MR) fluid used to fill in MR dampers after long-term cyclic operation. For testing purposes, a meandering MR valve was customized to create a double-ended MR damper in which MR fluid flowed inside the valve due to the magnetic flux density. The test was conducted for 170,000 cycles using a fatigue dynamic testing machine which has 20 mm of stroke length and 0.4 Hz of frequency. Firstly, the damping force was investigated as the number of operating cycles increased. Secondly, the change in viscosity of the MR fluid was identified as in-use thickening (IUT). Finally, the morphological observation of MR particles was undertaken before and after the long-term operation. From these tests, it was demonstrated that the damping force increased as the number of operating cycles increases, both when the damper is turn on (on-state) and off (off-state). It is also observed that the particle size and shape changed due to the long operation, showing irregular particles.

This paper presents the characterization and hysteresis modeling of magnetorheological (MR) damper with meandering type valve. The meandering type MR valve, which employs the combination of multiple annular and radial flow passages, has been introduced as the new type of high performance MR valve with higher achievable pressure drop and controllable performance range than similar counterparts in its class. Since the performance of a damper is highly determined by the valve performance, the utilization of the meandering type MR valve in an MR damper could potentially improve the damper performance. The damping force characterization of the MR damper is conducted by measuring the damping force as a response to the variety of harmonic excitations. The hysteresis behavior of the damper is identified by plotting the damping force relationship to the excitation displacement and velocity. For the hysteresis modeling purpose, some parts of the data are taken as the training data source for the optimization parameters in the neuro-fuzzy model. The performance of the trained neuro-fuzzy model is assessed by validating the model output with the remaining measurement data and benchmarking the results with the output of the parametric hysteresis model. The validation results show that the neuro-fuzzy model is demonstrating good agreement with the measurement results indicated by the average relative error of only around 7%. The model also shows robustness with no tendency of growing error when the input values are changed