Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15892
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXu X.
dc.contributor.authorElgamal M.
dc.contributor.authorDoddamani M.
dc.contributor.authorGupta N.
dc.date.accessioned2021-05-05T10:28:24Z-
dc.date.available2021-05-05T10:28:24Z-
dc.date.issued2020
dc.identifier.citationJournal of Composite Materials Vol. , , p. -en_US
dc.identifier.urihttps://doi.org/10.1177/0021998320973744
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15892-
dc.description.abstractPolymer 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. © The Author(s) 2020.en_US
dc.titleTailoring composite materials for nonlinear viscoelastic properties using artificial neural networksen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.