Artificial Neural Network Based Wear and Tribological Analysis of Al 7010 Alloy Reinforced with Nanoparticles of SIC for Aerospace Application

Abstract

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.