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 viscoelastic properties of graphite-based magnetorheological grease

This paper highlights the effect of graphite on the dynamic viscoelastic properties of magnetorheological grease (MRG). Two types of MRG namely MRG and graphite-MRG, GMRG with 0 wt.% and 10 wt. % of graphite respectively was synthesized by using a mechanical stirrer. The rheological properties of both sample at various magnetic field strength from 0 to 0.603 T was analyzed via rheometer under oscillatory mode with strain ranging from 0.001 to 1% with fixed frequency at 1 Hz for strain sweep and frequency ranging from 0.1 to 80 Hz at a constant strain of 0.01 % for frequency sweep. Based on the result obtained, the value of storage and loss modulus are dependent on the graphite content. A high value of storage modulus was achieved in the GMRG sample at all applied magnetic field strengths within all frequency ranges. These phenomena related to the contribution of graphite to forming the chain structure with CIPs and offered a more stable and stronger structure as compared with MRG. Moreover, the reduction in the value of loss modulus in GMRG was noticed compared to MRG at on-state conditions reflected by the stable structure obtained by GMRG. Lastly, both samples displayed a strong solid-like (elastic) behavior due to the high value of storage modulus, G’ acquired compared to loss modulus, G’’ at all frequency ranges. Therefore, the utilization of graphite in MRG can be used in wide applications such as brake and seismic dampers

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

Field-dependent viscoelastic properties of graphite-based magnetorheological grease

This paper highlights the effect of graphite on the dynamic viscoelastic properties of magnetorheological grease (MRG). Two types of MRG namely MRG and graphite-MRG, GMRG with 0 wt.% and 10 wt. % of graphite respectively was synthesized by using a mechanical stirrer. The rheological properties of both sample at various magnetic field strength from 0 to 0.603 T was analyzed via rheometer under oscillatory mode with strain ranging from 0.001 to 1% with fixed frequency at 1 Hz for strain sweep and frequency ranging from 0.1 to 80 Hz at a constant strain of 0.01 % for frequency sweep. Based on the result obtained, the value of storage and loss modulus are dependent on the graphite content. A high value of storage modulus was achieved in the GMRG sample at all applied magnetic field strengths within all frequency ranges. These phenomena related to the contribution of graphite to forming the chain structure with CIPs and offered a more stable and stronger structure as compared with MRG. Moreover, the reduction in the value of loss modulus in GMRG was noticed compared to MRG at on-state conditions reflected by the stable structure obtained by GMRG. Lastly, both samples displayed a strong solid-like (elastic) behavior due to the high value of storage modulus, G’ acquired compared to loss modulus, G’’ at all frequency ranges. Therefore, the utilization of graphite in MRG can be used in wide applications such as brake and seismic dampers

Modeling and simulation of vehicle steer by wire system

The steer by wire system offer many benefits compare with conventional steering system. By eliminating the mechanical linkage of column shaft between the steering wheel and the front wheel system, it gives more space efficiency, fuel efficiency in term of functionality and at the same time present challenges to the designer. Many researchers have done their control strategy on steer by wire system in past recent years. This paper presents the control strategy for the wheel synchronization and the variable steering ratio. Mathematical modeling was created for steering wheel and front wheel model. The steering wheel and the front wheel system is control using PID controller and introduce a new feedforward variable steering ratio based on under propensity equation method. A simulation was made and compared in order to analysis the system performance

Synthesis, characterization and magnetorheological properties of carbonyl iron suspension with superparamagnetic nanoparticles as an additive

Magnetorheological (MR) fluids are suspensions of micron-sized particles dispersed in carrier fluid. Due to high density magnetic particles, MR fluids are facing the problem with the instability of the suspension caused by high settling rate. Recently, researches have been conducted on the advantages of using the mixture of magnetic nanoparticles and microparticles, called bidisperse MR fluids. However, even though the sedimentation stability is improved, there is a reduction in dynamic yield stress when the nanoparticle is introduced. In this work, the investigation of magnetic iron nanoparticles (γ-Fe2O3) as an additive to magnetic carbonyl iron (CI) suspension has been proposed so as to improve the sedimentation stability and redispersibility, but at the same time enhance the MR performance. The results indicated that the addition of nanoparticles reduced the sedimentation rate, improved redispersibility and enhanced the rheological performance of MR fluids as the particle fill the voids between the microparticles and strengthened the interparticle chains contributing to well-arranged particle structure

The strain energy tuning of the shape memory alloy on the post-buckling of composite plates using finite element method

This paper presents a geometric non-linear finite element model of shape memory alloy composite plates and its source code in order to determine critical loads and to trace post-buckling paths of the composite plates. A numerical study was conducted on symmetric and anti-symmetric angle-ply and cross-ply composite plates. Buckling and post-buckling improvements of composite plates due to the shape memory effect behaviour of shape memory alloy were carried out. The pre-strained shape memory alloy wires were embedded within laminated composite plates so that recovery stress could be induced with the heated wires. The methods of active property tuning and active strain energy tuning were applied to show the various effects of the shape memory alloy on the studied behaviour. The result showed that significant improvements occurred in the critical loads and the post-buckling paths of the symmetric and anti-symmetric angle-ply and the symmetric cross-ply composite plates due to the active strain energy tuning method. In the case of the anti-symmetric cross-ply composite plate where bifurcation point did not exist, the post-buckling path was substantially improve

Mini review on effect of coatings on the performance of magnetorheological materials

Magnetorheological materials have attracted a great deal of interests nowadays due to its controllable mechanical properties upon the application of external magnetic field. Its ability to change its rheological properties in a split second has found its way in the applications that require absorption and isolation of vibration and noise. However, the problems with oxidation, sedimentation and aggregation of the magnetic particles hinder the optimum performance that can be utilised with this smart material. This includes the reduced performance of yield stress, shear stress, shear modulus and storage modulus and over a long operational period, will affect its magnetisation properties. Hence, there is a need to protect the magnetic particles with coating layer which can overcome these drawbacks. The main focus of this work is to present an overview on the aforementioned problems in MR materials that can be controlled by applying protective coating on the magnetic particles. Several works have reported the enhancement of performances such as oxidation resistance, interface between particles and the carrier medium as well as sedimentation stability by introducing coated magnetic particles in the MR materials

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors