Experimental study on the effect of nanoparticle concentration on the lubricating property of nanofluids for MQL grinding of Ni-based alloy

Abstract

The effect of nanoparticle concentration on the lubricating property of nanofluids for minimum quantity lubrication (MQL) grinding was experimentally investigated based on the status of nanofluid MQL. Different concentrations of nanofluids with molybdenum disulfide (MoS2), carbon nanotubes (CNTs), and their mixtures (MoS2-CNTs) were prepared. Difficult-to-cut Ni-based alloy was used as the workpiece material to explore the effect of mass fraction of nanofluids on grinding force ratio (G) and workpiece surface quality. The statistical analysis of grinding force ratio and surface roughness (Ra) were performed using one-way analysis of variance followed by Tukey’s post-hoc test with confidence intervals (CI) of 95%.Results showed that given the same mass fraction, MoS2-CNTs achieved lower G [GMix (8%)=0.274] and surface roughness (Ra=0.328μm) than MoS2 and CNTs. These characteristics were attributed to the physical collaboration of the mixed nanoparticles. For the same nanoparticle, G decreased initially, reaching the lowest value at 8% [GMix (8%)=0.274], and then increased with the increase in mass fraction of nanofluids because of the influence of agglomeration. Ra, increased gradually with the increase in viscosity of nanofluids. Autocorrelation analysis shows that MoS2-CNT nanofluid MQL achieved the lowest autocorrelation initial value (RMix=0.64) when τ=0. This result confirms that MoS2-CNT nanofluid MQL could improve machining precision and surface quality. Among different concentrations of nanofluids, the high-frequency continuous oscillation of the autocorrelation curve of the 8% MoS2-CNTs indicated improved workpiece surface quality. Combined friction coefficient, Ra, and autocorrelation analytical results show that 8% MoS2-CNTs was the optimum concentration for nanofluid MQL in the experiment. Autocorrelation analysis of the profile curves reveals the microstructural information of the workpiece surface and, combined with Ra analysis, provides a better method of analyzing the surface quality of a workpiece.