Artificial neural network approach to tailor composite materials with nonlinear viscoelasticity

Xianbo Xu, Mariam Elgamal, Nikhil Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Material characterization is a major challenge for viscoelastic composite materials. Due to nonlinearity of materials properties, it is overwhelming to test each composite material for every application case under the combined effects of temperature and strain rate. Machine learning methods can help by using the existing datasets to predict properties over a different combination of parameters. This work focuses on building an artificial neural network (ANN) architecture to help in predicting properties and compositions of viscoelastic materials. The high density polyethylene (HDPE) syntactic foam is used as a case study material. Four types of HDPE syntactic foams were tested using dynamic mechanical analysis (DMA). Then, ANN was used to build the master relation of viscoelastic properties with respect to frequency, temperature, particle volume percentage and strain. The master curve for storage modulus was then transformed to time domain relaxation function and used to predict the stress-strain relations. The elastic modulus was extracted and compared to experimental results from tensile tests. The results show good agreements in properties of syntactic foams with both tested and extrapolated compositions. These results show that ANN can help in designing composite materials using machine learning methods on a limited dataset.

Original languageEnglish (US)
Title of host publicationProceedings of the American Society for Composites - 35th Technical Conference, ASC 2020
EditorsKishore Pochiraju, Nikhil Gupta
PublisherDEStech Publications
Pages1157-1169
Number of pages13
ISBN (Electronic)9781605956657
StatePublished - 2020
Event35th Annual American Society for Composites Technical Conference, ASC 2020 - Virtual, Online
Duration: Sep 14 2020Sep 17 2020

Publication series

NameProceedings of the American Society for Composites - 35th Technical Conference, ASC 2020

Conference

Conference35th Annual American Society for Composites Technical Conference, ASC 2020
CityVirtual, Online
Period9/14/209/17/20

Keywords

  • Artificial neural network
  • Dynamic mechanical analysis
  • Machine learning
  • Syntactic foam
  • Viscoelasticity

ASJC Scopus subject areas

  • Ceramics and Composites

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