A data-driven rate and temperature dependent constitutive model of the compression response of a syntactic foam

Burcu Tasdemir, Vito L. Tagarielli, Antonio Pellegrino

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Polymeric syntactic foams are used in aerospace and marine applications requiring low density and low moisture absorption together with high specific strength and stiffness. Their mechanical response is highly sensitive to temperature and strain rate and such sensitivity must be modelled accurately. In this study, the uniaxial compressive response of a polymeric syntactic foam is measured at strain rates in the range [10-3, 2.5·103] /s and temperatures varying between -25°C and 100°C. The resulting dataset is used to train a neural network to predict the compressive response of the foam at arbitrary strain rates and temperatures. It is found that the surrogate model is highly effective in predicting the material response at temperature and rates not included in its training set. Finally, a stochastic version of the data-driven model to allow predictions of the variability in the stress versus strain response is proposed.
Original languageEnglish
Article number108790
JournalMaterials Today Communications
Volume39
DOIs
Publication statusPublished - 1 Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Foams
  • impact behaviour
  • Statistical properties/methods
  • mechanical testing
  • machine learning

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