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Artificial Neural Network for Modelling the Performance of Carbonyl-Iron Particle-Paraffin Oil-Based Magneto-Rheological Fluid

Authors: Emagbetere E, Zuokumor KC

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This work investigated the effectiveness of artificial neural network (ANN) for modelling the performance characteristics of a new magneto-rheological fluid (MRF). The MRF was developed from paraffin oil, carbonyl-iron particle and grease. The performance characteristics were investigated experimentally to ascertain the effects of magnetic field strength, the proportion by mass of carbonyl-iron particle and grease on the yield strength and the sedimentation ratio of the fluid. Then ANN was developed based on experimental data, and its performance was evaluated using R-square and mean square error values. The yield strength of the developed MRF was significantly affected by the percentage of carbonyl-iron particle added and the magnetic field strength. On the other hand, the proportion of carbonyl-iron particles and grease additive affected the sedimentation ratio. The developed ANN performed satisfactorily for all data sets used for training, validation and testing of the model. The error between predicted responses and those of experiments were very similar, having values of R-square close to 1. Overall, the artificial neural network was an effective tool for modelling the rheological performance of the magneto-rheological fluid.

Affiliations: Department of Mechanical Engineering, College of Engineering and Technology, Federal University of Petroleum resources, Effurun, Nigeria.
Keywords: Smart Material, Magnetic Particles, Grease, Rheological Properties, ANN Modelling
Published date: 2022/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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Chemical Engineering Department, Faculty of Engineering, University of Benin, PMB 1154, Ugbowo, Benin City, Edo State, Nigeria.