The current study investigates the wear behavior of three distinct composite compositions designated as C1,
C2, and C3, with direct implications for aerospace applications. Critical factors such as the Coefficient of Friction (Cf),
Specific Rate of Wear (Sw), and Frictional Force (FF) were meticulously analyzed using a systematic experimental
approach and the Taguchi L27 array design. Significant relationships between input factors and responses emerged after
subjecting these responses to Taguchi signal-to-noise ratio analysis. The optimal parameter combination of a 5%
composition, 14.5 N Applied Load (Ap), 150 rpm Rotational Speed (Rs), and 40.5 m Distance of Sliding (Ds) highlights
the interplay of factors in improving wear resistance. An Artificial Neural Network (ANN) was used as a predictive tool to
boost research efficiency, achieving an impressive 99.663% accuracy in response predictions. The result shows comparison
of the ANN's efficacy with actual experimental results. These findings hold great promise for aerospace applications where
wear-resistant materials are critical for long-term performance under harsh operating conditions. The incorporation of ANN
predictions allows for rapid material optimization while adhering to the stringent requirements of aerospace environments.
This research contributes to the evolution of tailored composite materials, poised to improve aerospace applications with
increased reliability, efficiency, and durability by advancing wear analysis methodologies and predictive technologies.