4.7 Article

Parameter Identification of Fractional Index Viscoelastic Model for Vegetable-Fiber Reinforced Composite

Journal

POLYMERS
Volume 14, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/polym14214634

Keywords

vegetable fiber-reinforced composite; viscoelasticity model; numerical method; material parameter determination

Funding

  1. CAPES-MES, Rio de Janeiro, Brazil [125/11]

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The study investigated the adaptation of the uniaxial three-element viscoelastic constitutive model with integer and fractional index derivatives to the mechanical evolution of an epoxy-composite material reinforced with long random henequen fibers. Results showed that the fractional index version presented the best accuracy in simulating the material behavior.
In the present work, parameters for adapting the behavior of the uniaxial three-element viscoelastic constitutive model with integer and fractional index derivatives to the mechanical evolution of an epoxy-composite material reinforced with long random henequen fibers, were determined. Cyclic loading-unloading with 0.1%, 0.2%, 0.3%, ..., 1.0% controlled strain and staggered fluency experiments at 5 MPa, 10 MPa, and 15 MPa constant tension were performed in stages, and the obtained data were used to determine and validate the model's parameter values. The Inverse Method of Identification was used to calculate the parameters, and the Particle Swarm Optimization (PSO) method was employed to achieve minimization of the error function. A comparison between the simulated uniaxial results and the experimental data is demonstrated graphically. There exists a strong dependence between properties of the composite and the fiber content (0 wt%, 9 wt%, 14 wt%, 22 wt%, and 28 wt% weight percentage fiber/matrix), and therefore also of the model parameter values. Both uniaxial models follow the viscoelastic behavior of the material and the fractional index version presents the best accuracy. The latter method was noted to be adequate for determination of the aforementioned constants using non-large experimental data and procedures that are easy to implement.

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