Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks

Xianbo Xu, Mariam Elgamal, Mrityunjay Doddamani, Nikhil Gupta

Research output: Contribution to journalArticlepeer-review


Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus (E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.

Original languageEnglish (US)
Pages (from-to)1547-1560
Number of pages14
JournalJournal of Composite Materials
Issue number11
StatePublished - May 2021


  • Dynamic mechanical analysis
  • artificial neural network
  • machine learning
  • nonlinear viscoelasticity
  • syntactic foam

ASJC Scopus subject areas

  • Ceramics and Composites
  • Mechanics of Materials
  • Mechanical Engineering
  • Materials Chemistry


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