Microscale and mesoscale modeling for compressive failure in unidirectional carbon fiber reinforced polymer composites with random distribution of fiber misalignments
Source abstract
Abstract
Abstract Compressive strength and failure mode of carbon fiber reinforced polymer (CFRP) composites are affected by the severity of fiber misalignment. In this paper, numerical models based on computational micromechanics are proposed to investigate the correlation between fiber misalignment and compression failure. A single‐fiber micromodel including fiber, matrix, and interface phases is first developed to capture the influence of misalignment severity on the material failure mode. The failure longitudinal stress distribution in the fiber predicted by the micromodel indicates that the failure mode depending on the initial misalignment can be identified as uniform compression failure, compression‐bending combination collapse, and fiber buckle. A mesoscale 2D model with multiple fibers is then constructed to investigate the kink band formation. This model takes into account the distribution of fiber misalignments to capture the key geometric features of fiber misalignment that influent the compression failure in realistic composite materials at the structural scale. The results show that fiber misalignment angle amplitude, misalignment dispersion (characterized by the standard deviation), and misalignment wavelength are the key parameters of fiber misalignment, which have significant effects on compression strength and kink band geometry. Highlights Single‐fiber model to capture the effect of misalignment severity on failure mode. Multi‐fiber model with randomly distributed fiber misalignments. Failure mode depending on the severity of initial misalignment was identified. Key parameters of misalignment on compression strength and kink band geometry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mechanical Behavior of Composites · Composite Material Mechanics · Numerical methods in engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
MOE Key Laboratory of Disaster Forecast and Control in Engineering, School of Mechanics and Construction Engineering Jinan University Guangzhou China
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