Exploitation of Artificial Intelligence Methods for Prediction of Atmospheric Corrosion

David Seidl; Zora Jančíková; Pavel Koštial; Ivan Ružiak; Ivan  Kopal; Katerina Kreislova

doi:10.4028/www.scientific.net/DDF.326-328.65

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HomeDefect and Diffusion ForumDefect and Diffusion Forum Vols. 326-328Exploitation of Artificial Intelligence Methods...

Exploitation of Artificial Intelligence Methods for Prediction of Atmospheric Corrosion

Abstract:

The contribution deals with the use of artificial neural networks for prediction of corrosion loss of structural carbon steel. Nowadays there is certain chance to predict a corrosion loss of materials by artificial intelligence methods, especially by neural networks. A model of neural network for prediction of corrosion loss of structural carbon steel based on the input environmental parameters affecting the corrosion of metals in the atmospheric environment (temperature, relative humidity, air pollution by sulphur dioxide and the exposition time) was created. The model enables to predict corrosion loss of steel with a sufficiently small error.

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Periodical:

Defect and Diffusion Forum (Volumes 326-328)

Pages:

65-68

DOI:

https://doi.org/10.4028/www.scientific.net/DDF.326-328.65

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Online since:

April 2012

Authors:

David Seidl, Zora Jančíková, Pavel Koštial, Ivan Ružiak, Ivan Kopal, Katerina Kreislova

Keywords:

Artificial Neural Network (ANN), Atmospheric Corrosion, Model, Prediction

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References

[1] K. Kreislová, M. Halama, D. Knotková, J. Van Lesebettens: Atmospheric Corrosion of Structural Metals – Methods of Prediction of Corrosion Attack, In Eurocorr 2008, Edinburg, UK, (2008).

Google Scholar

[2] P. Jančík, I. Pavlíková, J. Bitta: Ochrana ovzduší, No. 4, Vol. 21 (2009), pp.24-27.

Google Scholar

[3] Z. Jančíková: Artificial Neural Networks in Material Engineering, Ostrava, GEP ARTS, 2006, 81 p., ISBN 80-248-1174-X.

Google Scholar

[4] P. Švec, Z. Jančíková, J. Melecký, P. Koštial: Implementation of Neural Networks for Prediction of Chemical Composition of Refining Slag. In: International Conference on Metallurgy and Materials, 2010, pp.155-159, ISBN 978-80-87294-17-8.

Google Scholar

[5] Z. Jančíková, J. David, M. Vrožina: Hutnické listy, Num. 1, Vol. LXI (2008), pp.88-94.

Google Scholar