Experimental and numerical investigation of cooling effectiveness of nano minimum quantity lubrication

Abstract

The literature on nano-minimum quantity lubrication (NMQL) machining suggests that its effectiveness is due to the increased thermal conductivity of the base oil and the reduced friction at the cutting interface due to the rolling effect of the nanoparticles. However, the relative contribution of the increased nanofluid thermal conductivity and nanofluid lubricity on the performance of NMQL has not been investigated. This study presents an experimental and a numerical investigation of NMQL cooling effectiveness and compares it with that of pure minimum quantity lubrication (MQL). A lumped system analysis was carried out on a flat Inconel workpiece with different heat inputs. Two different heat sources, an aluminum electric resistor and a propane torch, were used for three different cooling strategies: dry, MQL and NMQL. A propane torch was used to replicate the machining operational conditions involving vaporization of the base oil. The experimental results showed minimal improvements in the cooling effectiveness and the heat transfer coefficient of NMQL cooling when compared to MQL. There was less than a 1 % reduction in the surface temperature of the workpiece when using NMQL cooling compared to that of MQL. Additionally, the increase in the heat transfer coefficient for a flow rate of 22 ml/h was 1.07 %, while an increase of 1 % was observed at a flow rate of 44 ml/h. The experimental results suggest that the benefits of nano minimum quantity lubrication observed in the literature could be mostly associated with the lubrication benefits of the nanoparticles. This conclusion was derived from the experimental comparisons of the cooling effectiveness of NMQL over MQL, which yielded only minor improvements. Additionally, this paper presents a novel multiphase numerical model for NMQL. This model considered nanoparticles as distinct discrete particles along with the oil droplets, and it captured the flow conditions, average surface temperatures, and average heat transfer coefficient. This CFD model estimated the surface temperatures with errors below 10 %, while the heat transfer coefficient values had an average error of 12.05 %. This is the first attempt in the open literature to model a nano minimum quantity lubrication and cooling strategy using a two-phase approach.