## 3 Results and Discussion
### Assessment of Surface Roughness  
The quality of the finished products can be controlled by controlling the surface roughness with the use of appropriate cutting variable values and lubrication strategies. Surface roughness should be evaluated to assess the functioning of machined components and determine whether the desired requirements are met under acceptable cutting and cooling conditions [26]. The surface roughness (Ra) of hard bearing steel in turning was evaluated under the ZnO nanofluid cooling condition by varying different control variables such as cutting speed, feed rate, depth of cut, and flow rate. The obtained Ra varied from  
\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 & 0.0003837 & 0.0001279 & 39.61 & 0.007 & 6.27 & Yes \\ s & 3 & 0.0003022 & 0.0001007 & 31.19 & 0.009 & 4.94 & Yes \\ v & 3 & 0.0046892 & 0.0015631 & 484.05 & 0.000 & 76.68 & Yes \\ q & 3 & 0.0007302 & 0.0002434 & 75.37 & 0.003 & 11.94 & Yes \\ Error & 3 & 0.0000097 & 0.0000032 & & & & \\ Total & 15 & 0.0061149 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ANOVA findings for VB.  
Figure 8: Impact of input variables on tool wear.  
Figure 9: Interaction effects of input variables on tool wear.  
0.252 \(\upmu\)m (test no. 9: ap = 0.3 mm, s = 0.05 mm/rev, v = 210 m/min, q = 50 mL/hr) to 2.174 \(\upmu\)m (test no. 16: ap = 0.4 mm, s = 0.2 mm/rev, v = 70 m/min, q = 40 mL/hr). The surface roughness was traced to be higher in magnitude when the feed rate was the highest (0.2 mm/rev). Also, Ra increased due to a gain in feed rate [27]. With an increase in feed rate, the volume of material increases, and material buildup in the form of chips occurs across the tool-work-chip interface. Consequently, rising friction increased the roughness of the surface. Heat transmission is very minimal at higher cutting rates due to insufficient time. With increasing temperature, the surface quality deteriorated [28].  
Moreover, the influence of input variables on surface roughness was discussed using the main effects plot, interaction plots, and ANOVA. According to the main effects diagram (Figure 10), the surface roughness increased with a growing feed rate. This may be because the distances among peaks and valleys expand as the feed rate per tooth rises, and in hard machining processes, surface roughness is mainly generated by cutting tool scratches on the machined work surface [29]. The value of surface roughness decreased with the slight gain in depth of cut (0.1 to 0.2 mm), beyond which it gradually improved at a lower rate. The surface roughness was almost the same when cutting speed changed from 70 to 140 m/min, but when the speed reached 210 m/min, the roughness deteriorated due to the softening of work material due to the relatively higher temperature generation at 210 m/min. Further, when the cutting speed was raised to 280 m/min, the surface roughness slightly improved due to the generation of a higher tool wear at the highest cutting speed. The effect of flow rate seemed to be very low, as the mean surface roughness varied along the mean line. Therefore, it can be said that, as the impact of other variables on surface roughness was higher, hence, the actual effect of flow rate on surface roughness was not clear. Thus, for a clear understanding of the effect of lubricant flow rate, all other variables should be fixed. The interaction plots for surface roughness (Figure 11) confirmed the relevant interaction effect of the pair of terms ap-q, s-q, v-q, and ap-v on surface roughness. An ANOVA (Table 3) approach was used to determine the percentage impact of input terms on Ra. The ANOVA affirmed that the flow rate had the least contribution, at about 2.26%, on the surface roughness, while the surface roughness was majorly dominated by the feed rate (74.26%), followed by the depth of cut (15.88%) and the cutting speed (7.13%).