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

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 conduc...

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Main Authors:	Bahiuddin, Irfan, A. Wahab, Nurul A., Shapiai, Mohd. I., Mazlan, Saiful A., Mohamad, Norzilawati, Imaduddin, Fitrian, Ubaidillah, Ubaidillah
Format:	Article
Language:	English
Published:	SAGE Publications Ltd 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/88849/1/IrfanBahiuddin2019_PredictionofField-DependentRheologicalProperties.pdf http://eprints.utm.my/id/eprint/88849/ http://dx.doi.org/10.1177/1045389X19844007
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spelling	my.utm.888492020-12-29T04:38:30Z http://eprints.utm.my/id/eprint/88849/ Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method Bahiuddin, Irfan A. Wahab, Nurul A. Shapiai, Mohd. I. Mazlan, Saiful A. Mohamad, Norzilawati Imaduddin, Fitrian Ubaidillah, Ubaidillah TA Engineering (General). Civil engineering (General) 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. SAGE Publications Ltd 2019-07-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/88849/1/IrfanBahiuddin2019_PredictionofField-DependentRheologicalProperties.pdf Bahiuddin, Irfan and A. Wahab, Nurul A. and Shapiai, Mohd. I. and Mazlan, Saiful A. and Mohamad, Norzilawati and Imaduddin, Fitrian and Ubaidillah, Ubaidillah (2019) Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method. Journal of Intelligent Material Systems and Structures, 30 (11). pp. 1727-1742. ISSN 1045-389X http://dx.doi.org/10.1177/1045389X19844007 DOI:10.1177/1045389X19844007
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Bahiuddin, Irfan A. Wahab, Nurul A. Shapiai, Mohd. I. Mazlan, Saiful A. Mohamad, Norzilawati Imaduddin, Fitrian Ubaidillah, Ubaidillah Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
description	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.
format	Article
author	Bahiuddin, Irfan A. Wahab, Nurul A. Shapiai, Mohd. I. Mazlan, Saiful A. Mohamad, Norzilawati Imaduddin, Fitrian Ubaidillah, Ubaidillah
author_facet	Bahiuddin, Irfan A. Wahab, Nurul A. Shapiai, Mohd. I. Mazlan, Saiful A. Mohamad, Norzilawati Imaduddin, Fitrian Ubaidillah, Ubaidillah
author_sort	Bahiuddin, Irfan
title	Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
title_short	Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
title_full	Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
title_fullStr	Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
title_full_unstemmed	Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
title_sort	prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method
publisher	SAGE Publications Ltd
publishDate	2019
url	http://eprints.utm.my/id/eprint/88849/1/IrfanBahiuddin2019_PredictionofField-DependentRheologicalProperties.pdf http://eprints.utm.my/id/eprint/88849/ http://dx.doi.org/10.1177/1045389X19844007
_version_	1687393630694670336
score	13.211869

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

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