A crucial characteristic of composites, which are manufactured from elements of
metal, is their mechanical and durability properties. A variety of reinforcing
agents and metal nanoparticles are used to create aluminum-based hybrid
metal-material composites. These composites are an advantageous alternative for
sectors with limited resources because of their robustness, wear resistance, and
thermal management capabilities. Manufacturing sectors employ Taguchi
optimisation and Grey relational analysis to enhance the mechanical and
durability properties of aluminum-based hybrid metal composites. To comprehend
the interrelationships between reinforcing materials such as
Al2O3 and SiC at constant fly ash concentration, five
responses such as wear loss, tensile strength, elongation rate, impact strength,
and hardness were considered and assessed. The Grey Relational Analysis (GRA)
method is used to optimise these responses and transform them into Grey
Relational Grade (GRG). The Grey Relational Grade (GRG) Analysis of Variance
(ANOVA) results were supplemented by a regression-based ANOVA analysis. SiC (p
value - 0.024) and Al2O3 (p value - 0.029) have been
demonstrated to be statistically significant in this situation. It was
determined that the correlation coefficient (R2), adjusted
correlation coefficient (Adj R2), Fischer's value (F-value), and
probability value (p-value for the probabilistic variables were found to be
81.43%, 70.29%, 7.31, and 0.028, respectively. All of the results have been
deemed satisfactory and fall within the recommended ranges. However, to confirm
the model's fit, a validation experimental run based on run 06 conditions
(A1B1C1) was performed. The experimental
grade that was predicted turned out to be 0.971. It was concluded that engineers
can fabricate an incredibly low combination of reinforcement materials and
applied load, Taguchi optimisation produces robust composite with minimal wear
loss, elongation rate, greater hardness, and tensile strength.