Verification of application of ANN modelling in study of compressive behaviour of aluminium sponges
Authors:
- Anna M. Stręk,
- Marek Dudzik,
- Arkadiusz Kwiecień,
- Krzysztof Wańczyk,
- Barbara Lipowska
Abstract
This article presents a preliminary neural network analysis of the compressive behaviour of aluminium open-cell sponges and answers the question of whether this phenomenon can be modelled using artificial intelligence. The research consisted of two phases: first – compression experiments, which in turn provided data for the second phase – the artificial neural network (ANN) analysis. A two-argument function was proposed and tested using the gathered experimental data with a two-layer feedforward network. The determination coefficient R2 for linear correlation between targets and modelling outputs was chosen as the criterion for the assessment of the quality of modelling. The obtained values were R2>0.96, which shows that neural networks hold the capacity to address the characterisation of the mechanical response of aluminium open-cell sponges in compression. Additionally, the mean absolute relative error (MARE) and the mean square error (MSE) were also determined.
- Record ID
- CUT0f07c8b9cdd84b2c94527b6c7ecba30d
- Publication categories
- ; ;
- Author
- Journal series
- Engineering Transactions (Rozprawy inżynierskie), ISSN 0867-888X, e-ISSN 2450-8071
- Issue year
- 2019
- Vol
- 67
- No
- 2
- Pages
- 271-288
- Other elements of collation
- fot.; rys.; tab.; wykr.; Bibliografia (na s.) - 285-288; Bibliografia (liczba pozycji) - 45; Oznaczenie streszczenia - Streszcz. ang.; Numeracja w czasopiśmie - Vol. 67, No 2
- Conference
- 41st Solid Mechanics Conference (41st SolMech 2018), 2018, 27-08-2018 - 31-08-2018, Warsaw, Polska
- Keywords in English
- metal sponges, aluminium sponges, compression tests, artificial neural networks
- DOI
- DOI:10.24423/EngTrans.991.20190615 Opening in a new tab
- URL
- http://et.ippt.gov.pl/index.php/et/article/view/991 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 70
- Additional fields
- Indeksowana w: Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT0f07c8b9cdd84b2c94527b6c7ecba30d/
- URN
urn:pkr-prod:CUT0f07c8b9cdd84b2c94527b6c7ecba30d
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.