2020
DOI: 10.1515/secm-2020-0024
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Effect of interphase parameters on elastic modulus prediction for cellulose nanocrystal fiber reinforced polymer composite

Abstract: In this study, the effective elastic modulus (Ec) of a cellulose nanocrystal fiber reinforced polymer composite was evaluated using the Mori-Tanaka and finite element (FE) model. The FE model was generated using a representative volume element with a periodic boundary condition. The mass fractions of the fiber in the composites (MFf) were set to 1, 2, and 3 wt.%. Elastic modulus values for interphase were input and were either uniform or exhibited a gradient. The E c for the uniform interphase region increased… Show more

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Cited by 10 publications
(5 citation statements)
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“…The fibers are randomly distributed in the square matrix and the cohesive element was inserted on the fiber boundary to simulate the interface mechanical behavior. By periodic boundary conditions and transverse tensile load, the transverse tensile strength of composites could be obtained [35,36], as shown in figure 7.…”
Section: Fe Modelmentioning
confidence: 99%
“…The fibers are randomly distributed in the square matrix and the cohesive element was inserted on the fiber boundary to simulate the interface mechanical behavior. By periodic boundary conditions and transverse tensile load, the transverse tensile strength of composites could be obtained [35,36], as shown in figure 7.…”
Section: Fe Modelmentioning
confidence: 99%
“…45 Although it is possible to assess the elastic modulus using these formulas, the limitation is that it does not consider interactions between adjacent inclusions and performs as a function of constituent parameters like volume percentage and modulus. 46 As a result, new theoretical models are required to more correctly predict nanocomposites behavior while accounting for the effect of interphase thickness.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, researchers have used other models to describe the mathematical properties of composites based on various parameters. Some of these models include the Proportional Hazards model, Kelly-Tyson model, Halpin-Tsai model, Bowyer-Bader model, Mori-Tanaka model, Levin model, Composite Cylinder Assemblage model, Shear-Lag model, Weibull Distribution model, and Hashin-Rosen model [16][17][18][19][20][21][22][23][24][25].…”
Section: Introductionmentioning
confidence: 99%