This work presents new constitutive models of a magnetorheological (MR) elastomer viscoelastic behavior using a machine learning method to predict the magnetic field dependent-stiffness and damping properties. The multiple output-models are formulated using two basic neural network models, which are artificial neural network (ANN) and extreme learning machine (ELM). These models are intended to capture the non-linear relationship between the inputs consisting of shear strain and magnetic flux density and outputs, which are storage modulus and loss factor. The optimized model is firstly identified by varying the model parameters, such as the number of hidden nodes and activation functions for both proposed prediction models. Then, the model performances were evaluated for training and testing data sets. The results showed that ANN and ELM prediction models had performed differently on two different outputs. The performance of the ANN prediction model was significant in predicting storage modulus where the root mean square error (RMSE) and coefficient of determination (R 2 ) of testing data out of modeling data sets were 0.012 MPa and 0.984 respectively. Meanwhile, the ELM model shows good agreement in predicting loss factor where the RMSE and R 2 were 0.007 MPa and 0.989, respectively. These machine learning-based models have successfully proved its high accuracy prediction that can be further applied to distinguish the linear viscoelastic (LVE) region and predict the damping properties of MR elastomer.

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.

Renewable energy is one of the alternative energy resources in Malaysia to replace fossil fuel use, which is an important issue that needs to be established. Some of the possible renewable energy sources are wind, hydro and solar. Since 2019, various incentives announced by the Malaysian Ministry of Energy and Natural Resources (KeTSA) to enhance renewable energy development which is in line with Government Energy policy. However, today, in contrast to the participation of investors and international contractors, the presence of entrepreneurs and domestic workers in this sector is feeble. In this regard, the Technical Vocational and Training (TVET) institution is seen to have the potential to minimize this crisis by creating competent, skilled and competitive electrical entrepreneurs for the field of renewable energy. This paper explores the ability of TVET electrical entrepreneurs to participate in renewable energy businesses. Based on the literature on energy entrepreneur development, it was found that the TVET electrical entrepreneur faces four challenges, namely financial, technology costs, logistics and government support. The proposed future development of renewable energy is in Mini-hydro and solar photovoltaic (PV), while wind power does not seem viable to TVET electricity entrepreneurs.

Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).