Citation

Imaduddin, Fitrian and Mohamad, Norzilawati and Mazlan, Saiful Amri and Shapiai, Mohd. Ibrahim and Bahiuddin, Irfan (2018) A Model of Magnetorheological Grease using Machine Learning Method. Key Engineering Materials, 775. pp. 191-197. ISSN 1662-9795

Text 10.4028@www.scientific.net@KEM.775.191.pdf - Published Version Restricted to Repository staff only Download (581kB)

Abstract

Magnetorheological (MR) grease is a promising material to replace MR fluid because the advantage in term of stability and less possibility to leaking. To improve the material properties, an accurate model can be critical for reducing the time and cost of the development process. A model has been developed to predict MR fluid material properties by including the composition. However, the model may need adjustment and cannot predict other essential rheology parameters, such as viscosity, apparent viscosity, shear rate, and shear stress. Therefore, the technical novelty of this paper is to propose a model with composition as one of the inputs using extreme learning machine method. A scoring system is also introduced to quantify the significance of the composition effect toward the MR grease performance. Then, the model is simulated and compared with experimental data. The performance shows high accuracy estimation with normalized root mean square error about 1.25%.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, magnetorheological grease, rheology, scoring system, carbonyl iron particle, weight percentage, composition
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	16 Aug 2021 15:22
Last Modified:	16 Aug 2021 15:22
URII:	http://shdl.mmu.edu.my/id/eprint/7698

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