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• W2912052484 abstract "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." @default.
• W2912052484 created "2019-02-21" @default.
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• W2912052484 date "2019-05-01" @default.
• W2912052484 modified "2023-10-04" @default.
• W2912052484 title "Application of machine learning to predict the multiaxial strain-sensing response of CNT-polymer composites" @default.
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• W2912052484 doi "https://doi.org/10.1016/j.carbon.2019.02.001" @default.
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