Insights into particle dispersion and damage mechanisms in functionally graded metal matrix composites with random microstructure-based finite element model.

This study investigates the impact of particle volume fraction and distribution on the deformation and damage of particle-reinforced metal matrix composites, particularly in the context of functionally graded metal matrix composites. In this study, a two-dimensional nonlinear random microstructure-based finite element modeling approach implemented in ABAQUS/Explicit with a Python-generated script to analyze the deformation and damage mechanisms in composites. The plastic deformation and ductile cracking of the matrix are captured using the Gurson-Tvergaard-Needleman model, whereas particle fracture is modelled using the Johnson-Holmquist II model. Matrix-particle interface decohesion is simulated using the surface-based cohesive zone method. The findings reveal that functionally graded metal matrix composites exhibit higher hardness values ( ) than traditional metal matrix composites. The results highlight the importance of functionally graded metal matrix composites. Functionally graded metal matrix composites with a Gaussian distribution and a particle volume fraction of 10% achieve values comparable to particle-reinforced metal matrix composites with a particle volume fraction of 20%, with only a 2% difference in . Thus, can be improved significantly by employing a low particle volume fraction and incorporating a Gaussian distribution across the material thickness. Furthermore, functionally graded metal matrix composites with a Gaussian distribution exhibit higher values and better agreement with experimental distribution functions when compared to those with a power-law distribution.