Optimization of Controlling Factors for TC4 Alloy With Al2O3–Graphene Hybrid Nanofluid

Abstract

Nowadays, mixing nanoparticles into cutting fluids is much more common to improve lubrication and cooling properties. Many studies have been carried out in the past to assess the machining performance using nanofluids. However, limited studies are based on hybrid nanoparticles. This work estimates TC4 alloy machining performance using a hybrid nanofluid. The minimum quantity lubrication (MQL) cooling technique is employed here to investigate machinability. The machining performance of TC4 alloy is estimated by taking surface roughness and cutting temperature as response parameters. Hybrid nanofluid is formed by adding nanoparticles of graphene into alumina (Al2O3) based nanofluid in a fixed volumetric proportion (20:80) and as base fluid, soybean oil is used. In addition, machining performance is investigated in terms of thermophysical properties by taking weight percent of concentrations of nanoparticles as 0.25, 0.50, 0.75, and 1.00, respectively. Significant improvements are observed in thermophysical properties with the hybridization of Al2O3 and graphene (Al2O3–GnP). Experimentation and parametric optimization are carried out using Taguchi's method. Controlling factors of hybrid nanofluid of alumina–graphene and monotype nanofluid of alumina are compared. The obtained results show that these parameters significantly reduce using hybrid nanofluid.