Specifications for modelling of the phenomenon of compression of closed-cell aluminium foams with neural networks
Authors:
- Anna M. Stręk,
- Marek Dudzik,
- Tomasz Machniewicz
Abstract
The article presents a novel application of the most up-to-date computational approach, i.e., artificial intelligence, to the problem of the compression of closed-cell aluminium. The objective of the research was to investigate whether the phenomenon can be described by neural networks and to determine the details of the network architecture so that the assumed criteria of accuracy, ability to prognose and repeatability would be complied. The methodology consisted of the following stages: experimental compression of foam specimens, choice of machine learning parameters, implementation of an algorithm for building different structures of artificial neural networks (ANNs), a two-step verification of the quality of built models and finally the choice of the most appropriate ones. The studied ANNs were two-layer feedforward networks with varying neuron numbers in the hidden layer. The following measures of evaluation were assumed: mean square error (MSE), sum of absolute errors (SAE) and mean absolute relative error (MARE). Obtained results show that networks trained with the assumed learning parameters which had 4 to 11 neurons in the hidden layer were appropriate for modelling and prognosing the compression of closed-cell aluminium in the assumed domains; however, they fulfilled accuracy and repeatability conditions differently. The network with six neurons in the hidden layer provided the best accuracy of prognosis at MARE ≤ 2.7% but little robustness. On the other hand, the structure with a complexity of 11 neurons gave a similar high-quality of prognosis at MARE ≤ 3.0% but with a much better robustness indication (80%). The results also allowed the determination of the minimum threshold of the accuracy of prognosis: MARE ≥ 1.66%. In conclusion, the research shows that the phenomenon of the compression of aluminium foam is able to be described by neural networks within the frames of made assumptions and allowed for the determination of detailed specifications of structure and learning parameters for building models with good-quality accuracy and robustness.
- Record ID
- CUTfb95c19ec6904a6b8296107b41b8ac34
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2022
- Vol
- 15
- No
- 3
- Pages
- [1-43]
- Article number
- 1262
- Other elements of collation
- fot.; rys.; tab.; wykr.; Bibliografia (na s.) - 40-43; Bibliografia (liczba pozycji) - 95; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 15, Iss. 3, Spec. Iss.
- Substantive notes
- Special Issue: Computational and Experimental Mechanics of Engineering Materials and Structures
- Keywords in English
- artificial neural network design, two-layered feedforward network, compressive behaviour, aluminium foam
- ASJC Classification
- DOI
- DOI:10.3390/ma15031262 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/15/3/1262 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTfb95c19ec6904a6b8296107b41b8ac34/
- URN
urn:pkr-prod:CUTfb95c19ec6904a6b8296107b41b8ac34
* 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.