Tribological Performance Optimization of Aluminium Cenosphere Syntactic Foams using Gaussian Process Regression and Sensitivity Analysis

This study investigates the dry sliding tribological behavior of aluminium syntactic foams reinforced with fly ash cenospheres. The composites were fabricated using stir casting with varying cenosphere contents (15%, 20%, 25%) and tested under different loads (5–25 N) and sliding velocities (4–8 m/s). Experimental results showed that increasing cenosphere content reduced specific wear rate from 12 × 10⁻⁶ to 7 × 10⁻⁶ grams per Newton meter, while the friction coefficient ranged from 0.46 to 0.56. A Gaussian Process Regression model was developed to predict wear behavior and achieved strong accuracy with R² of 0.92 and root mean square error of 4.0 × 10⁻⁷. Global sensitivity analysis using Sobol indices identified cenosphere content and applied load as the most influential parameters, contributing 49.6 percent and 47.5 percent, respectively, to the wear variation. Optimization through Pareto front analysis revealed that a combination of 25 percent cenosphere content, 20–25 N load, and 5–6 m/s velocity offers the best trade-off between low wear and stable friction. The findings provide a reliable design strategy for developing lightweight, wear-resistant aluminium-based syntactic foams suitable for industrial applications such as automotive and structural components.