Dispersion properties evaluation of copper-based nanoparticles as cutting fluid using Taguchi-based grey relational analysis

Abstract The objective of this study is to evaluate dispersion properties of copper-based nanoparticles in distilled water with sodium lauryl sulphate (SLS). A multifaceted approach is used to describe the optimized dispersion properties of copper-based nanoparticles as a cutting fluid based on responses such as thermal conductivity, surface tension, and viscosity. The experimental trials are conducted using Taguchi orthogonal arrays considering mass of nanoparticle, surfactant, and sonication time as input parameters. The results of this investigation shows that optimized dispersion properties of coolant are found to be at concentration 0.277 g of nanoparticle, 50 min of sonication time and 0.100 g of SLS. The optimum dispersion properties which are thermal conductivity 1.03 W/m–K, surface tension 34 MN/m and viscosity 1.68 centipoise revealed by Taguchi based grey relational grade (GRG). This study successfully validates that optimization of dispersion properties increases the thermal conductivity and decreases surface tension along with viscosity which provide strong support for effectiveness of the optimized dispersion properties of copper-based nanoparticles as a cutting coolant. Thus, the used copper-based nanoparticles in water with SLS results in a better coolant for machining than traditional coolants.