Determination of the thermal characteristics and temperature-dependent rheological properties of the magnetorheological elastomers (MREs) is of paramount importance particularly with regards to MRE applications. Hitherto, a paucity of temperature dependent analysis has been conducted by MRE researchers. In this study, an investigation on the thermal and rheological properties of epoxidized natural rubber (ENR)-based MREs was performed. Various percentages of carbonyl iron particles (CIPs) were blended with the ENR compound using a two roll-mill for the preparation of the ENR-based MRE samples. The morphological, elemental, and thermal analyses were performed before the rheological test. Several characterizations, as well as the effects of the strain amplitude, temperature, and magnetic field on the rheological properties of ENR-based MRE samples, were evaluated. The micrographs and elemental results were well-correlated regarding the CIP and Fe contents, and a uniform distribution of CIPs was achieved. The results of the thermal test indicated that the incorporation of CIPs enhanced the thermal stability of the ENR-based MREs. Based on the rheological analysis, the storage modulus and loss factor were dependent on the CIP content and strain amplitude. The effect of temperature on the rheological properties revealed that the stiffness of the ENR-based MREs was considered stable, and they were appropriate to be employed in the MRE devices exposed to high temperatures above 45 °C.

This study presents a laminatedmagnetorheological elastomer (MRE) isolator which applies to vibration control in practice. The proposed isolator is fabricated with multilayer MRE sheets associated with the natural rubber (NR) as a matrix, and steel plates. The fabricated MRE isolator is then magnetically analysed to achieve high magnetic field intensity which can produce high damping force required for effective vibration control. Subsequently, the NR-based MRE specimen is tested to identify the field-dependent rheological properties such as storage modulus with 60 weight percentage of carbonyl iron particles. It is shown from this test that the MR effect of MRE specimen is quantified to reach up to 120% at 0.8 T. Following the design stage, the electromagnetic simulation using the finite element method magnetic (FEMM) software is carried out for analysing the magnetic flux distribution in the laminated MRE isolator. The laminated MRE isolator is then examined to a series of compression for static and dynamic test under various applied currents using the dynamic fatigue machine and biaxial dynamic testing machine. It is shown that the static compression force is increased by 14.5% under strong magnetic field compared to its off-state. Meanwhile, the dynamic compression test results show that the force increase of the laminated MRE isolator is up to 16% and 7% for low and high frequency respectively. From the results presented in this work, it is demonstrated that the fullscale concept of the MRE isolator can be one of the potential candidates for vibration control applications by tunability of the dynamic stiffness.

Magnetorheological grease is seen as a promising material for replacing the magnetorheological fluid owing to its higher stability and the lesser production of leakage. As such, it is important that the rheological properties of the magnetorheological grease as a function of a composition are conducted in the modeling studies of a magnetorheological grease model so that its optimum properties, as well as the time and cost reduction in the development process, can be achieved. Therefore, this article had proposed a machine learning method-based simulation model via the extreme learning machine and backpropagation artificial neural network methods for characterizing and predicting the relationship of the magnetorheological grease rheological properties with shear rate, magnetic field, and its compositional elements. The results were then evaluated and compared with a constitutive equation known as the state transition equation. Apart from the shear stress results, where it had demonstrated the extreme learning machine models as having a better performance than the other methods with R 2 more than 0.950 in the training and testing data, the predicted rheological variables such as shear stress, yield stress, and apparent viscosity were also proven to have an agreeable accuracy with the experimental data.

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