Investigation on carbon fiber-reinforced polymer combined with graphene nanoparticles subjected to drilling operation using response surface methodology and non-dominated sorting genetic algorithm-II

In this article, drilling of carbon fiber-reinforced plastics combined with graphene nanoparticles is investigated. These nanocomposites have the potential to be used widely in different industrial applications such as electrical, biomedical and aerospace engineering due to their superior mechanical...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part E, Journal of process mechanical engineering Journal of process mechanical engineering, 2024-02
Main Authors: Baraheni, Mohammad, Hoseini, Ali Malek, Najimi, Mohammad Reza
Format: Article
Language:English
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Summary:In this article, drilling of carbon fiber-reinforced plastics combined with graphene nanoparticles is investigated. These nanocomposites have the potential to be used widely in different industrial applications such as electrical, biomedical and aerospace engineering due to their superior mechanical and thermal specifications. Nonetheless, to be assembled on different sets, drilling nanocomposites is required especially for rivets and joints. In drilling carbon fiber-reinforced polymers (CFRPs), delamination is one of the main challenges that should be minimized in order to enhance the produced hole quality. Besides, machining force is a chief factor to control the drilling-induced damages, especially delamination. This article evaluates the effects of machining parameters on machining force and delamination using response surface methodology experimental design and non-dominated sorting genetic algorithm-II (NSGA-II) multiobjective optimization methodology. Examining delamination is conducted in two parts including peel-up and push-down delamination which are the principal drawbacks in drilling of fiber-reinforced composites. Analysis of variance is also carried out in order to examine the effects of the machining parameters. In addition, predictive quadratic correlations are established by regression analysis. Eventually, multiobjective optimization was implemented by NSGA-II and the optimal values were obtained from pareto-optimal solution set aimed at enhancing the drilling process.
ISSN:0954-4089
2041-3009
DOI:10.1177/09544089241230160