In this study, Taguchi-Grey relational analysis was used to investigate and optimize wear parameters such as sliding speed,
reinforcement of Gr and reinforcement of Al2O3, and their effect on dry sliding wear performance of ZA-27 nanocomposites.
Nanocomposites were synthesized via hot pressing process with pre-processing mechanical milling. Sixteen experimental tests
were performed based on design of experiments which was created with the help of Taguchi L16 orthogonal array. Grey
relational analysis (GRA) was applied for determination of optimal combination of parameters in order to improve
tribological characteristics. Optimal combination of factors, obtained with Taguchi Grey relational analysis was sliding
speed of 100 rpm, reinforcement content of 1 vol.% Gr and reinforcement content of 4 vol.% Al2O3. Validation of results was
done by using Artificial Neural Network (ANN). Developed model had overall regression coefficient 0.99836, and output
values showed good correlation with experimental results. Based on this research, it can be observed that nanocomposites
with reinforcement of Gr and Al2O3 can be potentially employed in many industries as a good substitute for the base alloy. In
addition, as a result of the analysis of the worn surfaces, it was determined that with the increase of the Al2O3 ratio, the hard
Al2O3 nanoparticles turned the dominant wear mechanism into abrasive. Also, it was determined that the Gr nanoparticles
appeared on the abrasive wear lines.