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

Rajendra Pujari, Mageswari M, Herald Anantha Rufus N, Prabagaran S, Mahendran G, Saravanan R
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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.
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基于人工神经网络的航空航天SIC纳米颗粒增强Al - 7010合金磨损摩擦学分析
目前的研究调查了三种不同的复合材料(C1、C2和C3)的磨损行为,这对航空航天应用具有直接意义。采用系统的实验方法和Taguchi L27阵列设计,对摩擦系数(Cf)、比磨损率(Sw)和摩擦力(FF)等关键因素进行了细致的分析。在对这些响应进行田口信噪比分析后,发现输入因素与响应之间存在显著的关系。最佳参数组合为5%的成分、14.5 N的载荷(Ap)、150 rpm的转速(Rs)和40.5 m的滑动距离(Ds),突出了各种因素在提高耐磨性方面的相互作用。人工神经网络(ANN)被用作预测工具来提高研究效率,在响应预测方面达到了令人印象深刻的99.663%的准确率。结果与实际实验结果进行了比较。这些发现为航空航天应用带来了巨大的希望,因为在恶劣的操作条件下,耐磨材料对长期性能至关重要。人工神经网络预测的结合允许快速材料优化,同时坚持航空航天环境的严格要求。这项研究有助于定制复合材料的发展,通过推进磨损分析方法和预测技术,以提高可靠性、效率和耐用性,改善航空航天应用。
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