Predicting the multiaxial stress-strain behavior of polyethylene terephthalate (PET) at different strain rates and temperatures above Tg by using an Artificial Neural Network

Fei Teng, Gary Menary, Savko Malinov, Shiyong Yan, John Boyet Stevens

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

In this paper, an Artificial Neural Network (ANN) is used to predict the stress-strain behavior of PET at conditions relevant to Stretch Blow Moulding including large equibiaxial deformation and constant width deformation at elevated temperature and high strain rate for plane stress conditions. The input vectors considered are temperature(T), delta time (Δt),delta strain (Δε) in the both x & y directions, and stress(σ) in both directions with a corresponding output parameter of delta stress (Δσ) in both directions. In the present work, a feed-forward backpropagation algorithm was used to train the ANN. Predictions from the ANN were compared with experimental results showing that it was able to approximate the relationship between stress and strain during both equibiaxial and constant width stretch experiments at various strain rates & temperatures to a high degree of accuracy for all conditions tested.
Original languageEnglish
Article number104175
JournalMechanics of Materials
Volume165
Issue number5
Early online date20 Dec 2021
DOIs
Publication statusPublished - Feb 2022

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