Title :
Monitoring of influence of significant parameters during anodizing of aluminium
Author :
Michaf, P. ; Vagaska, Alena ; Gombar, Miroslav ; Hosovsky, Alexander ; Kmec, Jan
Author_Institution :
Dept. of Math., Inf. & Cybern., Tech. Univ. of Kosice, Presov, Slovakia
Abstract :
The paper deals with the possibilities of control the technological process of aluminium anodic oxidation using the Design of Experiments (DoE) and the higher order neural unit to monitor the influence of the significant parameters on the resulting AAO (anodic aluminium oxide) film thickness. It also compares the relationship between individual inputs factors and their mutual interactions on the AAO thickness at monitored current density of 1.00 A·dm-2 and 6.00 A·dm-2. The developed predicted model describes the influence of input factors on the final AAO thickness by cubic function and its reliability is 99.37 % at current density of 1 A·dm-2 and 99.47% at current density of 6 A·dm-2. The electrolyte temperature and the size of an applied voltage had the most important influence.
Keywords :
aluminium; aluminium manufacture; anodisation; design of experiments; neural nets; process monitoring; production engineering computing; DoE; aluminium anodic oxidation; anodic aluminium oxide; anodizing; design of experiments; electrolyte temperature; higher order neural unit; process monitoring; Aluminum; Current density; Predictive models; Thickness measurement; Training; Vectors; anodizing; layer thickness; neural unit; thin films;
Conference_Titel :
Applied Machine Intelligence and Informatics (SAMI), 2014 IEEE 12th International Symposium on
Conference_Location :
Herl´any
Print_ISBN :
978-1-4799-3441-6
DOI :
10.1109/SAMI.2014.6822447