Using Data Science to Evaluate Nano-Reinforced Epoxy Surfaces

Toughened composites reinforced with nanofillers show improved mechanical performance such as increased abrasion resistance, fracture toughness, and fracture energy. The degree of these improvements is influenced by the degree of dispersion of the nanofillers which can be analyzed using force microscopy (AFM), a technique that allows for mapping the local height and elastic modulus of a surface. However, current AFM apparatuses can only measure a narrow range of moduli according to the type of tip, which complicates the full-field measurement of moduli in nanocomposites with nanosilica (~72 GPa) embedded in epoxy (0.1 – 5 GPa). Moreover, height mapping can only visualize filler particles exposed at the surface. These limitations make it challenging to determine the 3D location of nanoparticles near the surface of a composite. To overcome these limitations of conventional AFM, we used a combination of data science, micromechanics, and experimental data from AFM to locate the centroidal position of nanosilica (NS) particles relative to the surrounding epoxy surface. Using finite element simulations, a theoretical dataset of modulus values as a function of particle position relative to the epoxy surface was created as a training set. Bayesian optimization determines the “best” particle position that results in minimum error between simulated and experimental modulus contours. The algorithm returns the 3D position of the fully or partially embedded NS particle relative to the epoxy surface. The algorithm has shown the ability to partially produce simulated modulus contours that resemble the experimental modulus contours.

This article appeared in the Proceedings of the American Society for Composites: Thirty-sixth Technical Conference, 2021. Lancaster, PA: DEStech Publications, Inc.

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Work Title Using Data Science to Evaluate Nano-Reinforced Epoxy Surfaces
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Open Access
Creators
  1. JONATHAN THEIM
  2. DANIEL P. COLE
  3. UTKARSH DUBEY
  4. ASHUTOSH SRIVASTAVA
  5. CHOWDHURY ASHRAF
  6. TODD C. HENRY,
  7. CHARLES E. BAKIS
  8. ANIRUDDH YASHISTH
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Destech Publications, Inc.
Publication Date September 20, 2021
Publisher Identifier (DOI)
  1. 10.12783/asc36/35815
Source
  1. American Society for Composites 2021
Deposited November 11, 2022

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  • Created

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Data science NS composite_ASC36 2021_Preprint-1.pdf

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator JONATHAN THEIM

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator DANIEL P. COLE

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator UTKARSH DUBEY

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator ASHUTOSH SRIVASTAVA

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator CHOWDHURY ASHRAF

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator TODD C. HENRY,

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator CHARLES E. BAKIS

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Added Creator ANIRUDDH YASHISTH

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Published

    November 11, 2022 15:17 by Scholarly Communications and Copyright

  • Updated Description Show Changes

    December 13, 2022 12:55 by xzx1

    Description
    • <jats:p>Toughened composites reinforced with nanofillers show improved mechanical performance such as increased abrasion resistance, fracture toughness, and fracture energy. The degree of these improvements is influenced by the degree of dispersion of the nanofillers which can be analyzed using force microscopy (AFM), a technique that allows for mapping the local height and elastic modulus of a surface. However, current AFM apparatuses can only measure a narrow range of moduli according to the type of tip, which complicates the full-field measurement of moduli in nanocomposites with nanosilica (~72 GPa) embedded in epoxy (0.1 – 5 GPa). Moreover, height mapping can only visualize filler particles exposed at the surface. These limitations make it challenging to determine the 3D location of nanoparticles near the surface of a composite. To overcome these limitations of conventional AFM, we used a combination of data science, micromechanics, and experimental data from AFM to locate the centroidal position of nanosilica (NS) particles relative to the surrounding epoxy surface. Using finite element simulations, a theoretical dataset of modulus values as a function of particle position relative to the epoxy surface was created as a training set. Bayesian optimization determines the “best” particle position that results in minimum error between simulated and experimental modulus contours. The algorithm returns the 3D position of the fully or partially embedded NS particle relative to the epoxy surface. The algorithm has shown the ability to partially produce simulated modulus contours that resemble the experimental modulus contours.</jats:p>
    • Toughened composites reinforced with nanofillers show improved mechanical performance such as increased abrasion resistance, fracture toughness, and fracture energy. The degree of these improvements is influenced by the degree of dispersion of the nanofillers which can be analyzed using force microscopy (AFM), a technique that allows for mapping the local height and elastic modulus of a surface. However, current AFM apparatuses can only measure a narrow range of moduli according to the type of tip, which complicates the full-field measurement of moduli in nanocomposites with nanosilica (~72 GPa) embedded in epoxy (0.1 – 5 GPa). Moreover, height mapping can only visualize filler particles exposed at the surface. These limitations make it challenging to determine the 3D location of nanoparticles near the surface of a composite. To overcome these limitations of conventional AFM, we used a combination of data science, micromechanics, and experimental data from AFM to locate the centroidal position of nanosilica (NS) particles relative to the surrounding epoxy surface. Using finite element simulations, a theoretical dataset of modulus values as a function of particle position relative to the epoxy surface was created as a training set. Bayesian optimization determines the “best” particle position that results in minimum error between simulated and experimental modulus contours. The algorithm returns the 3D position of the fully or partially embedded NS particle relative to the epoxy surface. The algorithm has shown the ability to partially produce simulated modulus contours that resemble the experimental modulus contours.