In the present study, the effect of reduction of cutting fluid consumption on the surface quality and tool wear was studied. Mathematical models were developed to predict the surface roughness using response surface methodology (RSM). Analysis of variance (ANOVA) was used to investigate the significance of the developed regression models. The results showed that the coefficient of determination values (R²) for the developed models was 97.46% for dry, 89.32% for flood mode (FM), and 99.44% for MQL, showing the high accuracy of fitted models. Also, under the minimum quantity lubrication (MQL) condition, the surface roughness improved by 23%–44% and 19%–41% compared with dry and FM, respectively, and the SEM images of machined surface proved the statement. The prepared SEM images of tool rake face also showed a considerable decrease in adhesion wear. Built-up edge and built-up layer were the two main products of the adhesion wear, and energy-dispersive X-ray spectroscopy (EDX) analysis of specific points on the tool faces helped to discover the chemical compositions of adhered materials. By changing dry and FM to MQL mode, dominant mechanism of tool wear in machining aluminum alloy was significantly decreased. Breakage wear that led to early failure of cutting edge was also controlled by MQL technique.

在本研究中，研究了减少切削液消耗对表面质量和工具磨损的影响。开发了数学模型以使用响应表面方法（RSM）预测表面粗糙度。方差分析（ANOVA）用于研究开发的回归模型的重要性。结果表明，测定值的系数（R ²对于已开发的模型，干模型为97.46％，泛洪模式（FM）为89.32％，MQL为99.44％，显示了拟合模型的高精度。同样，在最小量润滑（MQL）条件下，与干法和FM相比，表面粗糙度分别提高了23％–44％和19％–41％，加工表面的SEM图像证明了这一点。刀具前刀面的SEM图像也显示出粘附磨损显着降低。堆积的边缘和堆积的层是粘附磨损的两个主要产物，并且对工具面上特定点的能量色散X射线光谱（EDX）分析有助于发现粘附材料的化学成分。通过将干燥和FM更改为MQL模式，可显着降低铝合金加工中刀具磨损的主要机理。