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

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Abstract This study investigates the impact of $$\mathrm {Al_2O_3}$$ Al 2 O 3 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 $$\mathrm{AA6061\mbox{-}T6/Al_2O_{3}}$$ AA 6061 - T 6 / Al 2 O 3 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 ( $$\textrm{HRB}$$ HRB ) 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 $$\textrm{HRB}$$ HRB values comparable to particle-reinforced metal matrix composites with a particle volume fraction of 20%, with only a 2% difference in $$\textrm{HRB}$$ HRB . Thus, $$\textrm{HRB}$$ HRB 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 $$\textrm{HRB}$$ HRB values and better agreement with experimental distribution functions when compared to those with a power-law distribution.

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