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A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing

A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC)... Purpose – To discuss the effects of metal matrix composite (MMC) journal structure on the pressure distribution and, consequently, on the load‐carrying capacity of the bearing are predicted using feed forward architecture of neurons. Design/methodology/approach – The inputs to the networks are the collection of experimental data. These data are used to train the network using the Batch Back‐prop, Online Back‐prop and Quickprop algorithms. Findings – The neural network (NN) model outperforms the available experimental model in predicting the pressure as well as the load‐carrying capacity. Research limitations/implications – The experiment specimens used in this study have been made of MMC with aluminum based reinforced with SiC ceramic particles, using the stir casting technique. Various composite journal structures can be investigated. Practical implications – The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modelling journal bearing system. Originality/value – This paper discusses a new modelling scheme known as artificial NNs. An experimental and a NN approach have been employed for analysing MMC journal structure for hydrodynamic journal bearings and their effects on the system performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

A neural predictor to analyse the effects of metal matrix composite structure (6063 Al/SiCp MMC) on journal bearing

Industrial Lubrication and Tribology , Volume 58 (2): 15 – Mar 1, 2006

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Publisher
Emerald Publishing
Copyright
Copyright © 2006 Emerald Group Publishing Limited. All rights reserved.
ISSN
0036-8792
DOI
10.1108/00368790610651512
Publisher site
See Article on Publisher Site

Abstract

Purpose – To discuss the effects of metal matrix composite (MMC) journal structure on the pressure distribution and, consequently, on the load‐carrying capacity of the bearing are predicted using feed forward architecture of neurons. Design/methodology/approach – The inputs to the networks are the collection of experimental data. These data are used to train the network using the Batch Back‐prop, Online Back‐prop and Quickprop algorithms. Findings – The neural network (NN) model outperforms the available experimental model in predicting the pressure as well as the load‐carrying capacity. Research limitations/implications – The experiment specimens used in this study have been made of MMC with aluminum based reinforced with SiC ceramic particles, using the stir casting technique. Various composite journal structures can be investigated. Practical implications – The simulation results suggest that the neural predictor would be used as a predictor for possible experimental applications on modelling journal bearing system. Originality/value – This paper discusses a new modelling scheme known as artificial NNs. An experimental and a NN approach have been employed for analysing MMC journal structure for hydrodynamic journal bearings and their effects on the system performance.

Journal

Industrial Lubrication and TribologyEmerald Publishing

Published: Mar 1, 2006

Keywords: Neural nets; Metals; Composite materials; Algorithmic languages

References