Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different cooling-lubrication conditions using RSM method

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 (R-2) 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.