• Something wrong with this record ?

Usage of neural network to predict aluminium oxide layer thickness

P. Michal, A. Vagaská, M. Gombár, J. Kmec, E. Spišák, D. Kučerka,

. 2015 ; 2015 (-) : 253568. [pub] 20150402

Language English Country United States

Document type Journal Article, Research Support, Non-U.S. Gov't

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 anodic oxidation 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 deals with current densities of 1 A · dm(-2) and 3 A · dm(-2) for creating aluminium oxide layer.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc16028450
003      
CZ-PrNML
005      
20161031102104.0
007      
ta
008      
161005s2015 xxu f 000 0|eng||
009      
AR
024    7_
$a 10.1155/2015/253568 $2 doi
024    7_
$a 10.1155/2015/253568 $2 doi
035    __
$a (PubMed)25922850
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxu
100    1_
$a Michal, Peter $u Department of Mathematics, Informatics and Cybernetics, Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia.
245    10
$a Usage of neural network to predict aluminium oxide layer thickness / $c P. Michal, A. Vagaská, M. Gombár, J. Kmec, E. Spišák, D. Kučerka,
520    9_
$a 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 anodic oxidation 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 deals with current densities of 1 A · dm(-2) and 3 A · dm(-2) for creating aluminium oxide layer.
650    _2
$a oxid hlinitý $x chemie $7 D000537
650    _2
$a elektrolyty $7 D004573
650    _2
$a teoretické modely $7 D008962
650    12
$a neuronové sítě $7 D016571
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Vagaská, Alena $u Department of Mathematics, Informatics and Cybernetics, Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia.
700    1_
$a Gombár, Miroslav $u Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
700    1_
$a Kmec, Ján $u Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
700    1_
$a Spišák, Emil $u Department of Technologies and Materials, Faculty of Mechanical Engineering, Technical University of Košice, Mäsiarska 74, 042 00 Košice, Slovakia.
700    1_
$a Kučerka, Daniel $u Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
773    0_
$w MED00181094 $t TheScientificWorldJournal $x 1537-744X $g Roč. 2015, č. - (2015), s. 253568
856    41
$u https://pubmed.ncbi.nlm.nih.gov/25922850 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20161005 $b ABA008
991    __
$a 20161031102526 $b ABA008
999    __
$a ok $b bmc $g 1166764 $s 953080
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2015 $b 2015 $c - $d 253568 $e 20150402 $i 1537-744X $m TheScientificWorldJournal $n ScientificWorldJournal $x MED00181094
LZP    __
$a Pubmed-20161005

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...