Open AccessModeling of magnetorheological elastomer rheological properties using artificial neural network
Author(s) -
Kasma Diana Saharuddin,
Mohd Hatta Mohammed Ariff,
Irfan Bahiuddin,
Saiful Amri Mazlan,
Siti Aishah Abdul Aziz,
Nurhazimah Nazmi,
Abdul Yasser Abdul Fatah
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1051/1/012094
Subject(s) - viscoelasticity , elastomer , magnetorheological fluid , artificial neural network , mean squared error , materials science , magnetorheological elastomer , rheology , dynamic modulus , flexibility (engineering) , shear modulus , modulus , parametric statistics , dynamic mechanical analysis , viscosity , computer science , mathematics , structural engineering , composite material , artificial intelligence , engineering , statistics , polymer , damper
One of the important issues of Magnetorheological (MR) elastomer characteristic is to determine the linear viscoelastic region (LVE). This lead to the number of prediction models to predict the characteristic of MR elastomer based on its rheological properties. Based on the previous research, it is found that the availability of prediction model is regarded as one of the concerned issue in the development of MR elastomer material and devices. Therefore in this work, Artificial Neural Network (ANN) is proposed as prediction model due to its advantages particularly in terms of non-parametric modeling approach flexibility. Here, the shear strain and magnetic flux density are adopted as the inputs of the prediction model. Meanwhile the predicted outputs were the storage and loss modulus. The prediction model performance index was analyzed based on its generalization performance. Comparison between simulation result and experiment data shows good agreement where prediction on 0.80T of output storage modulus produced 0.0086MPa and 0.998 for Root Mean Square Error (RMSE) and Coefficient of Determination (R 2 ) respectively.