Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method

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 R2 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

Field dependent-shear stress prediction of magnetorheological fluid using an optimum extreme learning machine model

Extreme learning machine (ELM) application to model the shear stress of magnetorheological (MR) fluids has superiority over the existing methods, such as Herschel-Bulkley. Although the shear stress has been successfully predicted, the hidden node numbers are too high reaching up to 10,000 that will hinder the application of the models. Furthermore, the existing works have tried to determine the hidden node number only by trial and error method. Therefore, this paper aims to reduce the hidden node number by employing the particle swarm optimization (PSO) considering the accuracy and the hidden node numbers. The ELM based-shear stress model was firstly defined by treating the magnetic field and shear rate as the inputs and shear stress as output. The objective function optimization method was then formulated to minimize the normalized error and the hidden node numbers. Finally, the proposed methods were tested at various ELM activation functions and samples. The results have shown that the platform has successfully reduced the hidden node numbers from 10,000 to 571 while maintaining the error of less than 1%. In summary, the proposed objective function for PSO optimization has successfully built the optimum shear stress model automatically

Effects of petroleum-based oils as dispersing aids on physicochemical characteristics of magnetorheological elastomers

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

Analysis of EMG signals during stance and swing phases for controlling Magnetorheological Brake applications

The development of ankle foot orthoses (AFO) for lower limb rehabilitation have received significant attention over the past decades. Recently, passive AFO equipped with magnetorheological brake had been developed based on ankle angle and electromyography (EMG) signals. Nonetheless, the EMG signals were categorized in stance and swing phases through visual observation as the signals are stochastic. Therefore, this study aims to classify the pattern of EMG signals during stance and swing phases. Seven-time domains features will be extracted and fed into artificial neural network (ANN) as a classifier. Two different training algorithms of ANN namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) will be applied. As number of inputs will affect the classification performance of ANN, different number of input features will be employed. In this study, three participants were recruited and walk on the treadmills for 60 seconds by constant the speed. The ANN model was designed with 2, 10, 12, and 14 inputs features with LM and SCG training algorithms. Then, the ANN was trained ten times and the performances of each inputs features were measured using classification rate of training, testing, validation and overall. This study found that all the inputs with LM training algorithm gained more than 2% average classification rate than SCG training algorithm. On the other hand, classification accuracy of 10, 12 and 14 inputs were 5% higher than 2 inputs. It can be concluded that LM training algorithm of ANN was performed better than SCG algorithm with at least 10 inputs

Material characterizations of gr-based magnetorheological elastomer for possible sensor applications: rheological and resistivity properties

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

A Systematic Approach to Predict the Behavior of Cough Droplets Using Feedforward Neural Networks Method

Coronavirus disease 2019 (Covid-19) has been identified as being transmitted among humans with droplets from breath, cough, and sneezes. Understanding the droplets’ behavior can be critical information to avoid disease transmission, especially while designing a device deals with human air respiratory. Although various studies have provided enormous computational fluid simulations, most cases are too specific and quite challenging to combine with other similar studies directly. Therefore, this paper proposes a systematic approach to predict the droplet behavior for coughing cases using machine learning. The approach consists of three models, which are droplet generator, mask model, and free droplet model modeled using feedforward neural network (FFNN). The evaluation has shown that the three FFNNs models’ accuracies are relatively high, with R-values of more than 0.990. The model has successfully predicted the evaporation effect on the diameter reduction and the completely evaporated state, which can be considered unlearned cases for machine learning models. The predicted horizontal distance pattern also agrees with the data in the literature. In summary, the proposed approach has demonstrated the capability to predict the diameter pattern according to the experimental or previous work data at various mask face types

Application of Ant Colony Optimization for the Shortest Path Problem of Waste Collection Process

The search for the shortest path of the waste collection process is an interesting topic that can be applied to various cases, from a very practical basic problem to a complex automation system development. In a dense settlement, the waste collection system can be a challenging process, especially to determine the most optimized path. The obstacles can be circling streets, impassable roads, and dead-end roads. A wrong choice of method can result in wasteful consumption of energy. A possible method to solve the problem is the traveling salesman problem using ant colony search optimization, considering its relatively fast optimization process. Therefore, this paper proposes an application of ant colony and traveling salesperson problem in determining the shortest path of the waste collection process. The case study for the optimization algorithm application is the path UGM Sekip Lecturer Housing is considering. Firstly, the data was collected by measuring the distance between points. Then, the paths were modeled and then compared with the actual route used by waste transport vehicles. The last step is implementing the ant colony optimization and traveling salesman problem by determining the cost function and the parameters. The optimization process was conducted several times, considering the random generator within the algorithm. The simulation results show the probable shortest path with a value of about 752 meters so that the use of fossil fuels in waste transport vehicles can be more efficient. The results show that the algorithm can automatically recommend the minimized path length to collect waste

A new control-oriented transient model of variable geometry turbocharger

The flow input of a variable geometry turbocharger turbine is highly unsteady due to rapid and periodic pressure dynamics in engine combustion chambers. Several VGT control methods have been developed to recover more energy from the highly pulsating exhaust gas flow. To develop a control system for the highly pulsating flow condition, an accurate and valid unsteady model is required. This study focuses on the derivation of governing the unsteady control-oriented model (COM) for a turbine of an actively controlled turbocharger (ACT). The COM has the capability to predict the turbocharger behaviour regarding the instantaneous turbine actual and isentropic powers in different effective throat areas. The COM is a modified version of a conventional mean value model (MVM) with an additional feature to calculate the turbine angular velocity and torque for determining the actual power. The simulation results were further compared with experimental data in two general scenarios. The first scenario was simulations on fixed geometry positions. The second simulation scenario considered the nozzle movement after receiving a signal from the controller in different cases. The comparison between simulation and experimental results showed similarities in the recovered power behaviours the turbine inlet area increases or vice versa. The model also has proved its reliability to replicate general behaviour as in the example of ACT cases presented in this paper. However, the model is incapable to replicate the detailed and complicated phenomena, such as choking effect and hysteresis effect