Usage of Neural Network to Predict Aluminium Oxide Layer Thickness Article Swipe
Peter Michal
,
Alena Vagaská
,
Miroslav Gombár
,
Ján Kmeč
,
Emil Spišák
,
Daniel Kučerka
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/253568
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/253568
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2015/253568
- http://downloads.hindawi.com/journals/tswj/2015/253568.pdf
- OA Status
- gold
- Cited By
- 11
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1990293718
All OpenAlex metadata
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https://openalex.org/W1990293718Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2015/253568Digital Object Identifier
- Title
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Usage of Neural Network to Predict Aluminium Oxide Layer ThicknessWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-01-01Full publication date if available
- Authors
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Peter Michal, Alena Vagaská, Miroslav Gombár, Ján Kmeč, Emil Spišák, Daniel KučerkaList of authors in order
- Landing page
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https://doi.org/10.1155/2015/253568Publisher landing page
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https://downloads.hindawi.com/journals/tswj/2015/253568.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/tswj/2015/253568.pdfDirect OA link when available
- Concepts
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Anodizing, Electrolyte, Oxalic acid, Aluminium, Aluminium oxide, Layer (electronics), Oxide, Materials science, Chemical engineering, Inorganic chemistry, Metallurgy, Chemistry, Composite material, Electrode, Engineering, Physical chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
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2023: 3, 2021: 2, 2018: 1, 2017: 1, 2016: 3Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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