## 3 Results and Discussion
### Assessment of Tool Wear
Figure 4: Optical tool wear images obtained at the lowest depth of cut (0.1 mm). Figure 5: SEM with EDS test report of fresh tool tip. The wear analysis of the highest wear tool was also accomplished with the use of SEM and EDS test results (Figure 7). The element Fe was found to be the highest (69.9% by weight) on spot 1, which ensured the presence of element Fe into the chipped portion due to a micro-diffusion of the workpiece or the chip's material at an elevated temperature (214.9 \({}^{\circ}\)C). Also, the % of C was also improved due to the diffusion of C elements from the workpiece to the tool tip. In comparison to the fresh tool, decrements in the weight % of elements (N, Al, and Ti) were found due to the removal of the coating layer from spot 1 due to chipping. Furthermore, new elements like Cr, Mn, and Si were found on the tool tip due to micro-diffusion or the adhesion of the chip portion on the tool tip. Also, due to chipping at the edge, the coating was removed, and substrate element W was increasingly exposed; hence, the % of W was improved in comparison to the fresh tool. Further, another point on the tool edge (spot 2) was analyzed. The % of C was improved due to the adhesion and/or diffusion of the workpiece element. The % of N might have been enhanced due to the presence of a higher N content in the coating layer (TiAlN) on that spot. However, as the % of N was higher, the other elements of coating (Ti and Al) were reduced on that spot. The presence of workpiece elements (Cr, Mn, and Fe) on that spot ensured the diffusion or  
Figure 6: SEM with EDS test report of the lowest wear tool (run 6). adhesion mode of the mechanism in hard turning. The crater wear was clearly shown on the tool tip. It developed due to the removal of the coating layer from the rake surface of the workpiece. The removal of the coating layer occurred due to the higher temperature generated during machining. Further, the wear result was analyzed using main effects plot (Figure 8), interaction plots (Figure 9), and ANOVA (Table 2). Figure 8 shows that the tool wear was aggressively influenced by cutting speed as the curve was rising with speed. Considering individual effects, the average wear was found to be the least when the depth of cutting was set as moderate (0.3 mm), while 0.1 mm feed was attributed to the least average wear. Similarly, the highest flow rate (50 mL/hr) enabled machining attributed to the least average wear. The trend of wear was not clear with varying feed, depth of cutting, and flow rate. The effect of the pair of terms on tool wear was analyzed using interaction plots. The interaction effects of the pair of terms were relevant when the curve lines were parallel. The effects of interaction terms on tool wear deteriorated when the lines deviated from the parallel position. Based on this, the pair of parameters, namely s-ap, q-ap, and q-s, have relevant effects on tool wear, while other terms seem to be irrelevant (Figure 9). ANOVA (Table 2) results revealed that the cutting speed had the largest impact on tool wear (76.68%), succeeded by flow rate (11.94%), depth of cut (6.27%), and feed (4.94%). It also affirmed that all the input terms have significant consequences on tool wear. Figure 7: SEM with EDS test report of the highest wear tool (run 13).