Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/8617

TitleSome thoughts on neural network modelling of microabrasion–corrosion processes
Author(s)Mathew, M. T.
Pai, P. S.
Stack, M. M.
Rocha, L. A.
KeywordsTribocorrosion
microabrasion-corrosion process
artificial neural network (ANN)
multilayer perceptron (MLP)
radial basis function (RBF)
resource allocation network
Issue dateJul-2008
PublisherElsevier
JournalTribology International
Citation"Tribology International". ISSN 0301-679X. 41:7 (July 2008) 672-681.
Abstract(s)There is increasing interest in the interactions of microabrasion, involving small particles of less than 10 mm in size, with corrosion. This is because such interactions occur in many environments ranging from the offshore to health care sectors. In particular, microabrasion–corrosion can occur in oral processing, where the abrasive components of food interacting with the acidic environment, can lead to degradation of the surface dentine of teeth. Artificial neural networks (ANNs) are computing mechanisms based on the biological brain. They are very effective in various areas such as modelling, classification and pattern recognition. They have been successfully applied in almost all areas of engineering and many practical industrial applications. Hence, in this paper an attempt has been made to model the data obtained in microabrasion–corrosion experiments on polymer/steel couple and a ceramic/lasercarb coating couple using ANN. A multilayer perceptron (MLP) neural network is applied and the results obtained from modelling the tribocorrosion processes will be compared with those obtained from a relatively new class of neural networks namely resource allocation network.
TypeArticle
URIhttp://hdl.handle.net/1822/8617
DOI10.1016/j.triboint.2007.11.015
ISSN0301-679X
Publisher versionThe original publication is available at www.sciencedirect.com
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CIICS - Artigos em revistas de circulação internacional com arbitragem científica

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