Usage of neural network to predict aluminium oxide layer thickness.
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.
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.
Peter
P
Michal
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.
Alena
A
Vagaská
Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
Miroslav
M
Gombár
Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
Ján
J
Kmec
Department of Technologies and Materials, Faculty of Mechanical Engineering, Technical University of Košice, Mäsiarska 74, 042 00 Košice, Slovakia.
Emil
E
Spišák
Department of Mechanical Engineering, Institute of Technology and Businesses in České Budějovice, Okružní 10, 37001 České Budějovice, Czech Republic.
Daniel
D
Kučerka
D000537:Aluminum Oxide / Q000737:chemistry*; D004573:Electrolytes; D008962:Models, Theoretical; D016571:Neural Networks, Computer
D004573:Electrolytes; D000537:Aluminum Oxide