This paper investigated the effects of petroleum-based oils (PBOs) as a dispersing aid on the physicochemical characteristics of natural rubber (NR)-based magnetorheological elastomers (MREs). The addition of PBOs was expected to overcome the low performance of magnetorheological (MR) elastomers due to their inhomogeneous dispersion and the mobility of magnetic particles within the elastomer matrix. The NR-based MREs were firstly fabricated by mixing the NR compounds homogeneously with different ratios of naphthenic oil (NO), light mineral oil (LMO), and paraffin oil (PO) to aromatic oil (AO), with weight percentage ratios of 100:0, 70:30, 50:50, and 30:70, respectively. From the obtained results, the ratios of NO mixed with low amounts of AO improved the material physicochemical characteristics, such as thermal properties. Meanwhile, LMO mixed the AO led to the best results for curing characteristics, microstructure observation, and magnetic properties of the MREs. We found that the LMO mixed with a high content of AO could provide good compatibility between the rubber molecular and magnetic particles due to similar chemical structures, which apparently enhance the physicochemical characteristics of MREs. In conclusion, the 30:70 ratio of LMO:AO is considered the preferable dispersing aid for MREs due to structural compounds present in the oil that enhance the physicochemical characteristics of the NR-based MREs.
Strain localization is a significant issue that poses interesting research challenges in viscoelastic materials because it is difficult to accurately predict the damage evolution behavior. Over time, the damage mechanism in the amorphous structure of viscoelastic materials leads to subsequent localization into a shear band, gradually jeopardizing the materials' elastic sustainability. The primary goal of this study is to further understand the morphological effects and the role of shear bands in viscoelastic materials precipitated by strain localization. The current study aims to consolidate the various failure mechanisms of a sample and its geometry (surface-to-volume ratio) used in torsional testing, as well as to understand their effects on stress relaxation durability performance. A torsional shear load stress relaxation durability test was performed within the elastic region on an isotropic viscoelastic sample made of silicon rubber and a 70% weight fraction of micron-sized carbonyl iron particles. Degradation was caused by a shear band of localized plasticity that developed microscopically due to stress relaxation durability. The failure pattern deteriorated as the surface-to-volume ratio decreased. A field-emission scanning electron microscope (FESEM) and a tapping-mode atomic force microscope (AFM) were used for further observation and investigation of the sample. After at least 7500 cycles of continuous shearing, the elastic sustainability of the viscoelastic materials microstructurally degraded, as indicated by a decline in stress performance over time. Factors influencing the formation of shear bands were observed in postmortem, which was affected by simple micromanipulation of the sample geometry, making it applicable for practical implementation to accommodate any desired performance and micromechanical design applications.
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 study introduces a novel platform to predict complex modulus variables as a function of the applied magnetic field and other imperative variables using machine learning. The complex modulus prediction of magnetorheological (MR) elastomers is a challenging process, attributable to the material's highly nonlinear nature. This problem becomes apparent when considering various possible fabrication parameters. Furthermore, traditional parametric modeling methods are limited when applied to solve larger-scale cases involving large databases. Consequently, the application of non-parametric modeling such as machine learning has gained increasing attraction in recent years. Therefore, this work proposes a data-driven approach for predicting multiple input-dependent complex moduli using feedforward neural networks. Besides excitation frequency and magnetic flux density as operating conditions, the inputs consider compositions and curing conditions represented by magnetic particle weight percentage and the curing magnetic field, respectively. Extreme learning machines and artificial neural networks were used to train the models. The simulation results obtained at various curing conditions and other inputs confirm that the predicted complex modulus has high accuracy with an R2 of about 0.997, as compared to the experimental results. Furthermore, the predicted complex modulus pattern and magnetorheological effect agree with the experimental data using both the learned and unlearned data.