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Verification of application of ANN modelling in study of compressive behaviour of aluminium sponges

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Języki publikacji
EN
Abstrakty
EN
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 R 2 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 R 2 > 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.
Rocznik
Strony
271--288
Opis fizyczny
Bibliogr. 45 poz., rys., tab., wykr.
Twórcy
  • Cracow University of Technology Faculty of Civil Engineering Institute of Structural Mechanics Warszawska 24, 31-155 Kraków, Poland
autor
  • Cracow University of Technology Faculty of Electrical and Computer Engineering Department of Traction and Traffic Control Warszawska 24, 31-155 Kraków, Poland
  • Cracow University of Technology Faculty of Civil Engineering Institute of Structural Mechanics Warszawska 24, 31-155 Kraków, Poland
  • Foundry Research Institute Center for Design and Prototyping Zakopiańska 73, 30-418 Kraków, Poland
  • Institute of Ceramics and Building Materials Refractory Materials Division in Gliwice Toszecka 99, 44-100 Gliwice, Poland
Bibliografia
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4ce36461-fb8c-4963-a4df-96a0d841948f
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