Work

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Year: 2015

Type: article

Abstract: 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.

Source: The Scientific World JOURNAL

DOI: 10.1155/2015/253568

Authors: Peter Michal, Alena Vagaská, Miroslav Gombár, Ján Kmec, Emil Spišák, Daniel Kučerka

Institutions: Technical University of Košice, Institute of Technology and Business

Language: English

Topic: Anodic Oxide Films and Nanostructures

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $860

Funders: Slovenská Akadémia Vied, Vedecká Grantová Agentúra MŠVVaŠ SR a SAV