This study studied the low-velocity impact behavior of silicon carbide (SiC), boron carbide (B4C), and hybrid B4C/SiC reinforced aluminum 6061 alloy (Al 6061) matrix composites experimentally and numerically. The effect of hybridization, reinforcement volume fraction, and reinforcement type on low-velocity impact was investigated. 16 different metal-matrix composite materials were manufactured for the study, including 9 hybrid samples, 6 non-hybrid samples for each reinforcing ceramic particle, and 1 unreinforced sample. The powder metallurgy - hot press method was used in experimental production and then low-velocity impact tests were carried out. Numerical study was carried out using the non-linear finite element method (FEM). A Python algorithm running on ABAQUS/Explicit was utilized to determine ceramic particle distribution based on volume fraction, hybridization ratio, and random particle arrangement. The Johnson-Cook Plasticity Model was used for the non-linear relationship between stress and strain in the Al 6061 metal-matrix during impact. The results were interpreted with the contact force-time, contact force-displacement, and energy-time data obtained from low-velocity impact tests. In addition, the amount of collapse that occurred during the impact was measured and compared, and finally, SEM/EDX analysis was performed for the microstructure characterizations of the composite sample. Impact tests revealed that the collapse of composite samples ranged from 3.06mm to 3.58mm, with the unreinforced S6 sample collapsing 4.18mm, indicating that increased reinforcement elements reduce ductility, while a lower B4C ratio or higher SiC ratio in hybrid composites leads to greater collapse. Keeping the hybridization rate constant while increasing the reinforcement ratio leads to an increase in contact force and a decrease in contact time. Hybrid composites exhibited higher stiffness as SiC content increased, while B4C reinforced non-hybrid composites showed more significant stiffness. There is good agreement between the numerical and experimental results. Non-hybrid samples have a better match between numerical predictions and experimental results than hybrid samples.
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