## 3 Results and Discussion
### Assessment of Power Consumption  
In manufacturing units, most machining procedures are carried out with the help of electricity. The manufacturing industry is responsible for 36% of the world's CO\({}_{2}\) emissions and 30% of the world's energy use. Energy efficiency, power factor, active power, and active energy consumed by machines are significant responses that have a direct impact on sustainable machining [30]. From the measured power results, it can be said that the cutting power was the highest (1.388 kW obtained in test no. 7) when the cutting speed was the highest 280 m/min. It is a fact that the lathe motor needs more power to revolve the spindle at a faster speed. The motor uses significantly more power to keep the spindle speed set when cutting is being performed. It is noteworthy that prior research indicated that other cutting factors besides cutting speed are also responsible for changes in power usage [31; 32]. The main effect plots' results (Figure 12) indicated that the power consumption during machining was increasing with gains in cutting speed, feed, and depth of cut, while it was diminishing with increasing flow rate. The power consumption was greatly improved due to gaining speed as the motor consumed more power to rotate the workpiece at a higher speed [32]. Additionally, as the cutting speed increased, the mechanical and thermal load increased as well, which led to better power utilization [33]. Additionally, when the feed value increased, the amount of friction between the tool and work surface increased. This resulted in a larger cutting force, which increased cutting power consumption. Furthermore, when the cutting feed and depth grow, a larger cross-section of the workforce must be removed during the cutting process, increasing the need for a higher cutting force and energy use. This outcome agrees with the interaction plots' results (Figure 13). The improvement in flow rate enabled a much easier cutting due to the reduced friction between the tool and workpiece by applying the ZnO nano-cutting lubricant. The nanoparticles present in the nano-cutting lubricant may act as a rolling  
Figure 11: Interaction effects of input variables on surface roughness.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source** & **DF** & **Seq-SS** & **Adj-MS** & **F** & **P** & **\% Impact** & **Significant** \\ \hline ap & 3 & 0.56579 & 0.18860 & 35.85 & 0.008 & 15.88 & Yes \\ s & 3 & 2.64453 & 0.88151 & 167.57 & 0.001 & 74.26 & Yes \\ v & 3 & 0.25405 & 0.08468 & 16.10 & 0.024 & 7.13 & Yes \\ q & 3 & 0.08066 & 0.02689 & 5.11 & 0.107 & 2.26 & No \\ Error & 3 & 0.01578 & 0.00526 & & & & \\ Total & 15 & 3.56082 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA findings for Ra.  
agent, thus reducing the contact surface area between the tool and workpiece as a result friction; furthermore, cutting forces were abridged, thus enabling a much easier cutting with less power consumption. The cutting speed was the most dominant variable and was noticed as the graph line increased with a higher slope, resulting in faster changes in power consumption. Moreover, the interaction consequences of several terms, namely ap-s, s-q, and ap-q, on power consumption were found to be significant as their curves were randomly oriented, as displayed in Figure 13. Other terms such as ap-v, s-v, and v-q graphs were mostly parallel, which indicates the poor interactions for cutting power during the hard turning of AISI 52100 steel. Similar observations were traced in the literature [34]. Moreover, ANOVA was also developed (Table 4) to visualize the impact of input parameters on power consumption. From the ANOVA results, all the parameters were concluded to be significant with regard to power consumption, with cutting speed presenting the largest contribution (75.94%), succeeded by flow rate (13.89%), feed rate (6.02%), and depth of cut (3.78%).