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In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.

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The article presents the milled surface quality of Uni-Directional Carbon Fibre Reinforced Polymer (UD-CFRP) composites from Taguchi’s and grey relational analysis. The novelty is demonstrating the possibility of detecting the surface defects in polymer composites during milling using SEM analysis. The material used for this study is UD-CFRP composite laminates and made by hand-layup process. All the milling operations were carried out using a solid tungsten carbide end milling tool and experiments conducted on CNC milling machine. Taguchi L9, 3-level orthogonal array was considered for experimentation. Analysis of Variance (ANOVA) was conducted to explore the significance of each individual input process parameters on multiple performance characteristics. Optimal process parameters are thoroughly validated by grey relational grade achieved by the grey relational analysis for multi performance characteristics. Finally, experimental results were correlated and analyzed with scanning electron micrographs using Scanning Electron Microscope (SEM).

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