Alexandra Aulova (Avtor), Alen Oseli (Avtor), Marko Bek (Avtor)

Povzetek

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.

Ključne besede

PEEK;temperature;neural network;multilayer perceptron;constant stress rate;prediction;modeling;high-performance polymers;composites;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 539.3:678.7:004.8(045)
COBISS: 65892867 Povezava se bo odprla v novem oknu
ISSN: 0142-9418
Št. ogledov: 322
Št. prenosov: 139
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: temperatura;nevronska mreža;večslojni perceptron;konstantna hitrost napetosti;napovedovanje;modeliranje;visokozmogljivi polimeri;kompoziti;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-9
Zvezek: ǂVol. ǂ100
Čas izdaje: Aug. 2021
DOI: 10.1016/j.polymertesting.2021.107233
ID: 12982312