## 2 Optimization of MQL Parameters
### Results and Discussion  
An analysis was carried out to determine the effect of MQL parameters (_i.e._, fluid flow, pressure, and lubricant) on surface roughness. The statistical analysis was performed by using Minitab software, Version 16, (Minitab, Inc., Philadelphia, PA, USA, 2010). The \(S/N\) ratio obtained from Equation (1) and the result of \(R\)a is shown in Table 4. The three factors observed were lubricant, fluid flow, and pressure, respectively. Three levels of each factor were represented by \"1\", \"2\", and \"3\".  
The mean of \(S/N\) response for surface roughness of each level of parameters is shown in Table 5. Table 5 shows that the third level of lubricant, the third level of fluid flow, and the first level of pressure are ranked highest. Consequently, optimal MQL conditions for the experiment will be (3-3-1). The rank means that pressure is the most influential factor to surface roughness and the second influential factor is the lubricant introduced.  
Figure 2 indicated the \(S/N\) response graph. From the \(S/N\) response analysis, in order to get the best \(R\)a, the optimum MQL parameters were water soluble oil used for the lubricant, 50 mL/h for the flow rate, and 3 kg/cm\({}^{2}\) for the pressure. This result in the study is reasonably close to those in the related researches about optimization of MQL conditions to reduce tool wear such as the study of [PERSON] _et al._[13] and the study of [PERSON] _et al._[14]. Water soluble oil is considered to be the best lubricant available due to the inclusion of its non-flammable, good cooling, low viscosity and adequate wetting qualities. The flow rate of 50 mL/h and the pressure of 3 kg/cm\({}^{2}\) are suitable to mix lubricant and compressed air. Under this condition, an appropriate amount of mixture is sprayed into the cutting zone.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Number** & **Lubricant** & **Fluid Flow** & **Pressure** & \(R\)**a (\(\mu\)m)** & **S/N Ratio** \\ \hline
1 & 1 & 1 & 1 & 0.332 & 9.577 \\
2 & 1 & 2 & 2 & 0.355 & 8.995 \\
3 & 1 & 3 & 3 & 0.456 & 6.821 \\
4 & 2 & 1 & 2 & 0.351 & 9.094 \\
5 & 2 & 2 & 3 & 0.447 & 6.994 \\
6 & 2 & 3 & 1 & 0.289 & 10.782 \\
7 & 3 & 1 & 3 & 0.376 & 8.496 \\
8 & 3 & 2 & 1 & 0.239 & 12.432 \\
9 & 3 & 3 & 2 & 0.252 & 11.972 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The surface roughness result and \(S/N\) ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Lubricant** & **Fluid Flow** & **Pressure** \\ \hline
1 & 8.464 & 9.056 & 10.930 \\
2 & 8.957 & 9.474 & 10.020 \\
3 & 10.967 & 9.858 & 7.437 \\ Delta & 2.502 & 0.802 & 3.494 \\ Rank & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean of \(S/N\) response for surface roughness.  
Table 6 shows an analysis of variance for surface roughness. Based on an analysis of variance, pressure and lubricant are considered to be the most significant factors affecting the surface roughness. They contribute 68.127% and 30.189% to the total effect, respectively. The \(P\) index of lubricant (0.008) and the \(P\) index of pressure (0.004) less than 0.05 indicate that the effect of these factors has statistical significance. With \(R\)-Sq = 99.76%, it expresses that 99.76% of the variability in surface roughness is explained by the three given parameters of the MQL process.  
Because optimal MQL conditions for the experiment were determined to be (3-3-1) although not present in the experimental process, a verification experiment with optimized MQL parameters was performed to examine the results of the study. The result of the surface roughness of the verification test is 0.211 \(\upmu\)m for Ra. This value is less than the surface roughness of the experiment number 8 (3-2-1) that has the smallest value 0.239 shown in Table 4. It proves the result of the study is of value.