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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/559

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contributor.authorSahu, Anupama-
contributor.authorDas, Rojaleena-
contributor.authorSen, S-
contributor.authorMishra, S C-
contributor.authorSatapathy, Alok-
contributor.authorAnanthapadmanabhan, P V-
contributor.authorSreekumar, K P-
date.accessioned2007-12-04T04:22:41Z-
date.available2007-12-04T04:22:41Z-
date.issued2007-
identifier.citationInternational Conference on Advanced Materials and Composites (ICAMC-2007), Oct 24-26, 2007, P 741-746en
identifier.urihttp://hdl.handle.net/2080/559-
descriptionCopyright belongs to Proceeding publishersen
description.abstractPlasma sprayed alumina-titania (Al2O3-TiO2) coatings have many industrial applications. They provide a dense and hard surface coating which are resistant to abrasion, corrosion, cavitation, oxidation and erosion and are therefore regularly used for wear resistance, electrical insulation, thermal barrier applications etc. This work reports the implementation of Artificial Neural Networks (ANN) for analysis and prediction of wear behavior of plasma sprayed alumina titania composite coatings. Alumina pre-mixed with titania powder is deposited on mild steel substances by atmospheric plasma spraying at various operating power level and the coatings are subjected to solid particle erosion. ANNs are excellent tools for complex processes that have many variables and complex interactions. The analysis is made taking into account training and test procedure to predict the dependence of erosion wear behavior on angle of impact and velocity of erodent. This technique helps in saving time and resources for experimental trials.en
format.extent138561 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherNIIST, Trivandrumen
subjectPlasma Sprayingen
subjectAlumina-Titania Coatingen
subjectSolid Particle Erosionen
subjectNeural Networken
titleAl2O3-TiO2 Wear Resistant Coatings: A Neural Computationen
typeArticleen
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