Synthesis and predictive modelling of wear behaviour in hybrid aluminum composites

Aluminum, valued for its lightweight nature and versatile properties, finds wide use across industries. Yet, it has limitations: high ductility, lower hardness, and wear resistance can restrict its applications. This research explores the creation and analysis of a novel hybrid aluminum metal matrix composite (HAMMC) reinforced with in-situ ZrB₂ and fly ash. We used a multi-step stir-casting process to fabricate these HAMMCs. Then, their microstructure and fracture behaviour were characterized using X-ray diffraction, field emission scanning electron microscopy (FESEM), and energy dispersive spectroscopy (EDS). The findings revealed a 56 % boost in hardness for the HAMMCs compared to cast AA7075 aluminium when the primary reinforcement content reached 3 wt.%. Tensile strength also saw a substantial rise of 53.94 % with a 3 wt.% addition of ZrB₂, though this increased further to 5 wt.% led to a 30.18 % reduction. Also, linear reciprocating wear tests were conducted to understand wear behaviour, characterizing the worn surfaces with FESEM and a profilometer. Also performed a predictive analysis for wear rate and coefficient of friction and found that the Gaussian Process Regression emerges as the optimal model for both COF and SWR prediction for HAMMCs, achieving R² > 0.96 across targets.