Abstract
The integration of mesoscale modeling and macroscale experimentation has emerged as a promising approach for understanding and predicting the mechanical behavior and fatigue performance of fiber-reinforced polymer composites. In this work, the mean field homogenization technique is implemented to predict the fatigue performance of the carbon-fiber-reinforced polymer composites under cyclic loading conditions. To predict the number of fatigue cycles, Modified Gerber criteria are used with the stress-based Tsai–Hill failure indicator. Fatigue strength factor (α) and creep rupture strength factor (β) are experimentally evaluated and further implemented in a computational approach to predict fatigue life cycles of the composite. The effect of composite constituents, stress ratio, and loading direction are investigated in detail against the fatigue performance of the composite. Fatigue cycles are predicted at individual matrix and fiber levels at various stress ratios of 0.2, 0.4, 0.6, and 0.8 at different loading inclinations. The experimental results are compared with the mesoscale S–N curves.
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