Research Article Influence of Nanographite on Dry Sliding Wear Behaviour of Novel Encapsulated Squeeze Cast Al-Cu-Mg Metal Matrix Composite Using Artificial Neural Network L. Natrayan , 1 M. Ravichandran , 2 Dhinakaran Veeman , 3 P. Sureshkumar , 4 T. Jagadeesha, 5 and Wubishet Degife Mammo 6 1 Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105 Tamil Nadu, India 2 Department of Mechanical Engineering, K.Ramakrishnan College of Engineering, Samayapuram, 621112 Tamil Nadu, India 3 Centre for Additive Manufacturing and Computational Mechanics, Chennai Institute of Technology, Chennai, 600069 Tamil Nadu, India 4 Department of Mechanical Engineering, Ramco Institute of Technology, Virudhunagar, 626125 Tamil Nadu, India 5 Department of Mechanical Engineering, National Institute of Technology, Calicut, 673601 Kerala, India 6 Mechanical Engineering Department, Wollo University, Kombolcha Institute of Technology, Kombolcha, South Wollo, 208 Amhara, Ethiopia Correspondence should be addressed to L. Natrayan; natrayanphd@gmail.com, Dhinakaran Veeman; dhinakaranv@citchennai.net, and Wubishet Degife Mammo; wubishetdegife7@gmail.com Received 8 September 2021; Revised 20 October 2021; Accepted 1 November 2021; Published 19 November 2021 Academic Editor: Lakshmipathy R Copyright © 2021 L. Natrayan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This paper investigates the dry sliding wear behaviour of squeeze cast Al-Cu-Mg reinforced with nanographite metal matrix composites. The experimental study employed the Taguchi method. The Taguchi method plays a significant role in analyzing aluminium matrix composite sliding tribological behaviour. Specifically, this method was found to be efficient, systematic, and simple relative to the optimization of wear and friction test parameters such as load (10, 20, and 30), velocity (0.75, 1.5, and 2.25 m/s), and nanographite (1, 3, and 5 wt%). The optimization and results were compared with the artificial neural network. An orthogonal array L27 was employed for the experimental design. Analysis of variance was carried out to understand the impact of individual factors and interactions on the specific wear rate and the coefficient of friction. The wear mechanism, surface morphologies, and composition of the composites have been investigated using scanning electron microscopy with energy-dispersive X-ray spectroscopy. Results indicated that wt% addition of nanographite and increase of sliding speed led to a decrease in the coefficient of friction and wear rate of tested composites. Furthermore, individual parameter interactions revealed a smaller impact. The interactions involved wt% of nano-Gr and sliding speed, sliding speed and normal load, and wt% of nano-Gr and normal load. This inference was informed by the similarity between the results obtained ANN, ANOVA, and the experimental data. 1. Introduction In modern society, there is an increasing demand for new hybrid composites. Particularly, the demand emphasizes lightweight alloys. Most previous studies indicate that this demand trend comes from construction price decrease, mass construction reduction, and working life increase [1]. One of the examples of the demand concerns the use of aluminium and its associated alloys to substitute steel and materials sim- ilar to the latter. Notably, the demand for aluminium alloys has arisen from the affirmation that the alloys exhibit good mechanical properties [2, 3]. However, some studies caution that the alloys have poor tribological characteristics. Imper- ative to highlight is that tribological characteristics concern wear, lubrication, and friction of interacting surfaces, espe- cially those found to be in relative motion [4]. Hindawi Journal of Nanomaterials Volume 2021, Article ID 4043196, 14 pages https://doi.org/10.1155/2021/4043196