Application of machine learning to predict the multiaxial strain-sensing response of CNT-polymer composites

We present predictive multiscale models of the multiaxial strain-sensing response of conductive CNT-polymer composites. Detailed physically-based finite element (FE) models at the micron scale are used to produce training data for an artificial neural network; the latter is then used, at macroscopic...

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Bibliographic Details
Published inCarbon (New York) Vol. 146; pp. 265 - 275
Main Authors Matos, Miguel A.S., Pinho, Silvestre T., Tagarielli, Vito L.
Format Journal Article
LanguageEnglish
Published New York Elsevier Ltd 01.05.2019
Elsevier BV
Subjects
Artificial intelligence
Artificial neural networks
Computing time
Damage detection
finite element analysis
Finite element method
Machine learning
Mechanical analysis
Nanotubes
Neural networks
Polymer matrix composites
Polymers
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Summary:We present predictive multiscale models of the multiaxial strain-sensing response of conductive CNT-polymer composites. Detailed physically-based finite element (FE) models at the micron scale are used to produce training data for an artificial neural network; the latter is then used, at macroscopic scale, to predict the electro-mechanical response of components of arbitrary shape subject to a non-uniform, multiaxial strain field, allowing savings in computational time of six orders of magnitude. We apply this methodology to explore the application of CNT-polymer composites to the construction of different types of sensors and to damage detection. [Display omitted]
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ISSN:0008-6223
1873-3891
DOI:10.1016/j.carbon.2019.02.001