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.
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).
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