Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F15%3A00000473" target="_blank" >RIV/75081431:_____/15:00000473 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1155/2015/253568" target="_blank" >http://dx.doi.org/10.1155/2015/253568</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1155/2015/253568" target="_blank" >10.1155/2015/253568</a>

Result language
angličtina
Original language name
Usage of Neural Network to Predict Aluminium Oxide Layer Thickness
Original language description
This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodicoxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage) and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation.The paper also dealswith current densities of 1Axdm-2 and 3Axdm-2 for creating aluminium oxide layer.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
CG - Electrochemistry
OECD FORD branch
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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