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Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks

by Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 4
Year of Publication: 2012
Authors: Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta
10.5120/9678-4105

Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta . Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks. International Journal of Computer Applications. 60, 4 ( December 2012), 4-8. DOI=10.5120/9678-4105

@article{ 10.5120/9678-4105,
author = { Haider M. Mohammad, Nawal J. Hammadi, Rafil M. Lafta },
title = { Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 4 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number4/9678-4105/ },
doi = { 10.5120/9678-4105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:43.128755+05:30
%A Haider M. Mohammad
%A Nawal J. Hammadi
%A Rafil M. Lafta
%T Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 4
%P 4-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Precorroded steel A-106 B specimens were prepared at different surface roughness. These specimens were immersed in corrosive ferric chloride solution in different concentrations (1. 5, 3. 0, 4. 5, 6. 0% wt. ) at specified durations to initiate primarily the pitting corrosion. The corrosion pits distribution depend on the corrosive concentration, degree of surface roughness, and immersion duration. The pits were characterized using metallurgical microscope. Also, The pitting characteristics aimed to be predicted by "Artificial Neural Networks" (ANNs). The results obtained of pit quantification by ANNs predictions are shown to be agreed well against experimental values. i. e. R2=0. 9839

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Index Terms

Computer Science
Information Sciences

Keywords

Pitting corrosion Artificial neural network ANN and surface roughness

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