Generation of artificial 2-D fiber reinforced composite microstructures with statistically equivalent features

Statistically equivalent, artificial microstructures with similar fiber morphologies to as-manufactured scans are commonly used in micromechanical modeling. Features such as fiber clusters and matrix pockets are impor-tant as they may influence macroscale failure. In this study, a method of generating statistically equivalent artificial microstructures to experimental scans using local fiber volume fraction, fiber clusters, and matrix pockets was examined. 3000 artificial microstructures were created with a generator by randomly sampling input parameters which changed the fiber morphology. Fiber cluster and matrix pocket areas, densities, and orientations were used to characterize microstructures by sorting neighboring fiber triads. Experimental scans were used validate inputs from the artificial microstructure generator. Results showed the microstructures generated produced descriptors within range of the experimental scans. Microstructures were generated to match different descriptors of scanned specimens. First only local volume fraction was matched, and results compared to scans, then all descriptors were matched and compared.

Automated summary

This study examines a method of generating statistically equivalent artificial microstructures to experimental scans using local fiber volume fraction, fiber clusters, and matrix pockets. The generated microstructures were validated against experimental scans and showed good agreement with the descriptors.