A machine learning approach to modelling temperature-dependent cyclic behaviour
File(s)
Published version
Author(s)
sverdrup, fredrik
pellegrino, antonio
Tagarielli, Vito
tasdemir, burcu
Type
Journal Article
Abstract
This study presents a methodology for developing a temperature-dependent cyclic plasticity surrogate model as an efficient alternative to phenomenological temperature-dependent constitutive models. The titanium alloy Ti-6Al-4V, known for its widespread use in various engineering applications, was selected for this investigation. The surrogate model, based on a feedforward neural network, was trained using random amplitude stress-strain histories at various temperatures. To generate the training dataset, constitutive models were calibrated at specific temperatures using both experimental and available literature data, enabling the simulation of virtual temperature-dependent experiments. Cyclic loading simulations were performed at random axial strains within the range [-4 %, 4 %] and temperatures of 20℃, 400℃, 500℃, and 600℃. The predictive accuracy of the surrogate model was evaluated using unseen random stress-strain histories and temperature conditions, demonstrating high accuracy and computational efficiency.
Date Issued
2025-04-01
Date Acceptance
2025-03-27
Citation
Materials Today Communications, 2025, 45
ISSN
2352-4928
Publisher
Elsevier
Journal / Book Title
Materials Today Communications
Volume
45
Copyright Statement
© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.mtcomm.2025.112369
Publication Status
Published
Article Number
112369
Date Publish Online
2025-03-29