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

• Published in: Scientific reports 2024-09, Vol.14 (1), p.20835-20, Article 20835
• Main Authors: Naguib, M. E., Gad, S. I., Megahed, M., Agwa, M. A.
• Format: Article
• Language: English
• Subjects: 639/166/984; 639/166/988; 639/301/1023; 639/705/1042; 639/705/794; Aluminum oxide; Damage behavior; Dissolution; Finite element method; Functionally graded metal matrix composites; Heat treating; Humanities and Social Sciences; Metals; multidisciplinary; Normal distribution; Random microstructure-based model; Science; Science (multidisciplinary); Spherical indentation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-70247-3

This study investigates the impact of 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 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 ( 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 HRB values comparable to particle-reinforced metal matrix composites with a particle volume fraction of 20%, with only a 2% difference in HRB . Thus, 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 HRB values and better agreement with experimental distribution functions when compared to those with a power-law distribution.