Computational damage analysis of metal matrix composites to identify optimum particle characteristics in indentation process

Abstract

This paper presents an integrated numerical model that investigates the effects of Al2O3 particle volume fraction, distribution, size, shape, and clustering on the deformation, damage, and failure behaviors of particle-reinforced metal matrix composites. Additionally, the effects of the cohesive strength of the matrix-particle interface and the initial void volume fraction on these behaviors are investigated. In this study, random microstructure-based finite element modeling approach is used. The Gurson-Tvergaard-Needleman model is used to capture the plastic deformation and ductile cracking of the matrix, whereas the Johnson–Holmquist II model is used to model particle fracture. The matrix-particle interface decohesion is modeled using the surface-based cohesive zone method. A two-dimensional nonlinear finite element model augmented with Python-generated code is developed in ABAQUS/Explicit to analyze the damage mechanisms in AA6061-T6/Al2O3 particle-reinforced metal matrix composites. The results show that Al2O3 particle enhance the ability of particle-reinforced metal matrix composites to resist deformation. However, this enhancement is dependent on the indentation location, particulate characteristics, and the applied load. An increase in the particle volume fraction increases the risk of particle fracture, particularly when particles are close to the material surface. Circular particles are the optimal choice for the reinforcement of particle-reinforced metal matrix composites. The predictions are in good agreement with the experimental data, particularly in terms of Rockwell hardness and deformation characteristics.