Article ID Journal Published Year Pages File Type
7176717 Journal of Materials Processing Technology 2016 51 Pages PDF
Abstract
Results showed that given the same mass fraction, MoS2-CNTs achieved lower G [GMix (8%) = 0.274] and surface roughness (Ra = 0.328 μm) 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.
Related Topics
Physical Sciences and Engineering Engineering Industrial and Manufacturing Engineering
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