Investigation of nano-copper added cutting fluid performance on milling process by using the Bees Algorithm and experimental design methods

In this study, the tribological performance of copper nanoparticles coated with different ligands added to the metal cutting fluid as an additive was investigated. Copper, which has a high heat transfer coefficient and self-lubricating properties, is aimed at reducing the temperature in the chip area and reducing wear by acting as a lubricant, increasing tool life and surface quality of the processed material. The study was carried out in four stages. In the first stage, UV absorbance, STEM, wettability, and PSA analyses were performed on gelatine, cellulose, and PVP-coated copper nanoparticles synthesised by the chemical reduction method. According to the results of the analysis, the best ligand was gelatine. In the second stage, a pin-on-disc wear test was performed under different speeds (0.5, 0.7, and 0.9 m s−1), loads (10, 20, and 35 N), and ligand parameters. According to the friction coefficient, weight loss, and 3d topography images obtained from the wear experiments, the ligands that showed the best performance were gelatine, PVP, and cellulose, respectively. In the third stage, the chip removal process was carried out using the response surface method at different speeds, feeds, depths, and nano-fluid environments. In the 3d topography analyses, the best result in terms of surface roughness was obtained in the boron solution, and the best result in cutting forces was obtained in the gelatine-coated copper nanoparticle (CuNP-Gel) environment. In the tool wear experiment, boron solution and CuNP-Gel were used as coolant/lubricant liquids, and it was observed that the tool wear occurs more slowly in the CuNP-Gel environment.