Abstract

In the present work, a phase-field fracture model is developed to create an experimentally validated, physically motivated, and computationally tractable framework to predict the orientation-dependent complex fracture response of the unidirectional fiber-reinforced polymer matrix composites (PMC). The damage mechanism in PMC is many-fold, such as matrix micro-cracking, fiber breakage or pullout, fiber debonding, and delamination. The progressive damage and failure in the composite can be attributed to the competition of all these different damage mechanisms individually or in concert, depending on fiber volume fraction and orientation of the plies. Moreover, other important factors at the constituent level, such as viscoelasticity or plasticity of the matrix, can significantly influence the constitutive behavior of the composite. In this work, a homogenized, coupled thermo-mechanical constitutive model is developed considering the viscoelasticity of the polymer matrix for the fiber-reinforced PMCs. Subsequently, a phase-field fracture model is adopted to capture the essential damage mechanisms as matrix cracking and fiber pullout/breakage for unidirectional composite lamina. The phase-field variable considers an anisotropic tensorial term in its gradient to account for the orientation-dependent fracture of fiber-reinforced PMC. The model is numerically implemented by writing an ABAQUS user-element subroutine (UEL). The model can predict the direction-dependent damage propagation and the final load-deformation response at fracture in commercially acquired unidirectional glass-fiber-reinforced epoxy composites at different fiber orientations in reasonable agreement with the experiments. Based on the decoupling of the driving energies the model can also identify the dominant damage mechanism among the two such as fiber breakage or matrix cracking, for a given fiber orientation. Simulations are also conducted to validate the model predictions for carbon-fiber reinforced epoxy lamina with available experimental data in the literature.

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