Microstructure dependent transverse strength criterion for UD-CFRP composites via computational micromechanics and machine learning

The transverse strength of unidirectional carbon fiber reinforced polymer (UD-CFRP) composites is a high dimensional and nonlinear function of microstructure due to the wide scatter in mechanical properties and complex failure mechanisms, which is a challenging task to develop a general microstructure dependent strength criterion (MDSC) in theory or computation. Volume fraction and distribution of fibers are among the crucial influencing factors. A computational micromechanics and machine learning (ML) combined method is employed to uncover the transverse mechanical response of UD-CFRP composites. High-throughput finite element analyses (FEA) are performed to obtain the transverse behaviors of composites with varying fiber distribution and volume fraction under different loading states. They showed that fiber distribution has different effects on strengths in different failure modes, while the failure modes are closely related to loading states and fiber volume fractions. An ML model is then trained to characterize the relations between composite microstructure and composite strength. Then, the transverse strengths of 1000 new microstructures are predicted, which shows good agreement with FEA results, so that the MDSC of UD-CFRP is constructed by fully accounting for the influence of fiber distribution. Reliability of the method is verified by considering composites with various fiber volume fractions.