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

Matos, Miguel A.S.; Pinho, S.T.; Tagarielli, V.L.

doi:10.1016/j.carbon.2019.02.001

Cited by 87 publications

(37 citation statements)

References 33 publications

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“…Recently, machine learning has emerged as an attractive tool to reduce model complexity. 132 As a subset of machine learning, ANNs have been successfully used for a variety of applications, 133 some including structural analysis 134 and material science, ranging from studies of atomic properties 135 to the mechanical properties of individual CNTs. 136…”

Section: Development Of Articial Neural Networkmentioning

confidence: 99%

“…144 A further study conducted by the same authors included a non-uniform multiaxial strain eld. 132 Fakhrabadi et al 141 developed an ANN to predict the natural frequencies of CNTs with different types, lengths, and diameters and the effects of the attached masses on the rst ve natural frequencies of a special CNT. The well-known molecular mechanics and FE models are used to describe the vibrational behavior of this discrete structure before and aer attaching a particle on it.…”

Section: Applications Of Annsmentioning

confidence: 99%

“…Nonetheless, ANNs may be used also for predicting electrical conductivity of smart concrete, similar to the model used for conductive nanomodied polymer materials. 132,144 The idea of using smart materials can become more attractive by building a neural network able to predict properties of the specic nanomodied concrete, making it more cost-friendly and open for unexperienced engineers.…”

Section: Introductionmentioning

confidence: 99%

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Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Kekez

Kubica

2020

RSC Adv.

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Section: Development Of Articial Neural Networkmentioning

confidence: 99%

Section: Applications Of Annsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Kekez

Kubica

2020

RSC Adv.

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“…The past few years have seen a rapid development of data science and machine learning techniques, and these are increasingly being applied in materials engineering 7–13 . In most of these studies one-to-one relationships between microstructure and material properties are determined.…”

Section: Introductionmentioning

confidence: 99%

Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

Pathan

Ponnusami

Pathan

et al. 2019

Sci Rep

119

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We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as well as knowledge of the constitutive models for fibres and matrix, without performing physically-based calculations. The computational framework is based on evaluating the 2-point correlation function of the images of 1800 microstructures, followed by dimensionality reduction via principal component analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane. A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree regression model with 10-fold cross-validation strategy. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures.

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“…In recent years, carbon nanotubes (CNTs) have received much attention, and they are widely used in potential applications ranging from large-scale structures in automobiles to nanometer scale electronics. Because of their remarkable properties, carbon nanotubes are used in a variety of applications individually or in nanocomposite, such as cement [1], ceramics [2] and polymer composites [3,4]. Polymer nanocomposites based on CNTs are of great interest in both research and industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Characterization of PVC/MWCNTs Nanocomposite: Solvent Blend

Naim

Alfannakh

Arafat

et al. 2019

Science and Engineering of Composite Materials

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Polyvinyl Vinyl Chloride (PVC) multiwall carbon nanotubes (MWCNTs) nanocomposite flexible films were prepared using the solvent blend technique. Chloroform (CHCl3) and tetrahydrofuran ((CH2)4O) were used as solvents for MWCNTs and PVC, respectively. The effect of the solvents’ blend on electrical, optical and thermal properties of PVC/MWCNTs were investigated. The results of the Raman spectrum showed that all the characteristic bands of PVC polymer have a slight shift due to addition of MWCNTs. Electrical results showed that the nanocomposite samples with chloroform volume ratios of 10% and 25% had nearly the same conductivity. This is attributed to the formation of the MWCNTs network, which assisted in electrical conductivity. The I-V hysteresis curve decreases as the temperature increases and as it approaches the glass transition temperature. The non-isothermal kinetics analysis for PVC and PVC/MWCNTs were investigated by Thermogravimetry Analysis (TGA) using the model-free kinetic method. The non-isothermal measurements were carried out at five heating rates of 5 to 40∘C/min. The results show that the main decomposition process has constant apparent activation energies for all samples. The use of the bi-solvent method has improved the dispersion of untreated MWCNTs, and this has been reflected on the stability of both electrical and thermal properties.

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

Cited by 87 publications

References 33 publications

Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

Characterization of PVC/MWCNTs Nanocomposite: Solvent Blend

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