Neural networks for predicting the temperature-dependent viscoelastic response of PEEK under constant stress rate loading

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

Abstract

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.

Keywords

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

Data

Language:	English
Year of publishing:	2021
Typology:	1.01 - Original Scientific Article
Organization:	UL FS - Faculty of Mechanical Engineering
UDC:	539.3:678.7:004.8(045)
COBISS:	65892867
ISSN:	0142-9418
Views:	322
Downloads:	139
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	temperatura;nevronska mreža;večslojni perceptron;konstantna hitrost napetosti;napovedovanje;modeliranje;visokozmogljivi polimeri;kompoziti;
Type (COBISS):	Article
Pages:	str. 1-9
Issue:	ǂVol. ǂ100
Chronology:	Aug. 2021
DOI:	10.1016/j.polymertesting.2021.107233
ID:	12982312