Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

CG - Elektrochemie

OECD FORD obor

—

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Scientific World Journal

ISSN

2356-6140

e-ISSN

—

Svazek periodika

vol. 2015

Číslo periodika v rámci svazku

únor

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—