Spraying parameter optimization and microtopography evaluation in nanofluid minimum quantity lubrication grinding

Abstract

To overcome environmental problems, nanofluid minimum quantity lubrication (NMQL) has been utilized as a burgeoning alternative technique to conventional flood machining, which consumes a large quantity of cutting fluid. Furthermore, spraying parameter optimization is a prerequisite of an effective NMQL grinding and a key to achieving a new sustainable grinding process. However, the spraying parameters of NMQL are multifarious and have varying degrees of influence on lubri-cooling performance. The main objective of this work is to first obtain good spraying parameters in NMQL grinding through the analysis of signal-to-noise ratio and analysis of variance based on orthogonal experiment results. Furthermore, an experimental verification is carried out on the basis of the relative optimization of several groups of spraying parameters from the orthogonal experiments. Power spectral density function (PSDF) and energy spectrum are used to analyze the workpiece and debris microtopography. Consequently, the optimal spraying parameter is obtained. The spraying parameter (palm oil as the base oil, 2 vol% nanoparticle concentration, 0.6 MPa air pressure, and 0.4 gas–liquid flow ratio) achieved the lowest proportionality coefficient (0.245) of PSDF in the low-frequency band and the highest (0.142) in the high-frequency band. Meanwhile, the optimal grinding performance is validated by the workpiece and debris microtopography and long-strip debris morphology.