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  • Neural networks for predicting the temperature-dependent viscoelastic response of PEEK under constant stress rate loading
    Aulova, Alexandra ; Oseli, Alen ; Bek, Marko
    High-performance polymer composites are used in demanding applications in civil and aerospace engineering. Often, structures made from such composites are monitored using structural health monitoring ... systems. This investigation aims to use a multilayer perceptron neural network to model polymer response to a non-standard excitation under different temperature conditions. Model could be implemented into health monitoring systems. Specifically, the neural network was used to model PEEK material's creep behavior under constant shear stress rate excitation at different temperatures. Optimal neural network topology, the effect of the amount of training data and its distribution in a temperature range on prediction quality were investigated. The results showed that based on the proposed optimization criterion, a properly trained neural network can predict polymeric material behavior within the experimental error. The neural network also enabled good prediction at temperatures where stress-strain behavior was not experimentally determined.
    Vir: Polymer testing. - ISSN 0142-9418 (Vol. 100, Aug. 2021, str. 1-9)
    Vrsta gradiva - članek, sestavni del
    Leto - 2021
    Jezik - angleški
    COBISS.SI-ID - 65892867

    Povezava(-e):

    https://www.sciencedirect.com/science/article/pii/S0142941821001835?via%3Dihub
    Repozitorij Univerze v Ljubljani – RUL
    DOI

vir: Polymer testing. - ISSN 0142-9418 (Vol. 100, Aug. 2021, str. 1-9)
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