In statistical analysis, Least Square Fitting R2 is used to describe the model fit. RSM method suggested that both the surface roughness and tool wear fit linear models with relatively high R2 of 92% and 99%. The linear models representing the two responses can be described as functions of the cutting fluid concentration (c), cutting speed (v), feed rate (f) and cutting tool type, and are expressed as in equation [2]. The coefficients' values for the surface roughness and tool wear (for different tool types) are shown in Table 2 and Table 3.
![](https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1617643566.jpg)
Table 2
Response surface model coefficients for the values of surface roughness.
Tool Type
|
H10A
|
GC1115
|
H13A
|
Coefficient
|
Surface roughness model
|
Surface roughness model
|
Surface roughness model
|
bo
|
+ 0.59048
|
+ 0.30825
|
-0.017302
|
b1
|
+ 9.00000E-003
|
+ 9.00000E-003
|
+ 9.00000E-003
|
b2
|
-3.09524E-003
|
-3.09524E-003
|
-3.09524E-003
|
b3
|
+ 6.27778
|
+ 6.27778
|
+ 6.27778
|
Table 3
Response surface model coefficients for the values of tool wear
Tool Type
|
H10A
|
GC1115
|
H13A
|
Coefficient
|
Tool wear model
|
Tool wear model
|
Tool wear model
|
bo
|
+ 14.06408
|
+ 2.57963
|
-9.13259
|
b1
|
+ 0.15178
|
+ 0.15178
|
+ 0.15178
|
b2
|
+ 0.62489
|
+ 0.62489
|
+ 0.62489
|
b3
|
+ 19.03333
|
+ 19.03333
|
+ 19.03333
|
Table 4 shows the analysis of variance (ANOVA) F-values, p-values and percentage contribution ratio (PCR) for each of the studied process parameters for the surface roughness and tool wear. In statistical significance testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was observed, assuming that the null hypothesis is correct. The null hypothesis (which assumes that all parameters have no significant effect) is rejected when the p-value is less than the predetermined significance level, which is 0.05 (95 per cent confidence level). This means that any factor has p-value less than 0.05 is considered to be a significant model parameter. This study indicated that the surface roughness was affected by the cutting speed, feed rate, and tool type, while the tool wear was affected by the fluid concentration, cutting speed, feed rate, and tool type. Also, the F-value gives a relative measure of the significance of the examined parameters. PCR is obtained for each parameter by dividing the squares term of this parameter by the total sum of squares and multiplying by 100. The higher the F-value and PCR, the stronger the effect of a given factor. It was clear that the feed rate had the most significant impact on the surface roughness among all the examined factors, owing to the largest F-value and PCR of 145 and 44%, respectively.
Moreover, the tool type and cutting speed were of less significance (especially the latter), with F-values of 68 and 28, respectively, and PCR of 41% and 8%. Finally, the effect of cutting fluid concentration on the surface roughness was shown to be insignificant. The ANOVA results had also demonstrated a remarkable influence of the cutting speed on the tool wear (F-value = 7140 and PCR = 85%). Tool type comes the second with F-value of 622 and PCR of 15%. Lastly, and despite the model's significant factors, both the fluid concentration and feed rate had relatively trivial effects on the tool wear with F-values of 5 and 8, respectively and PCR of only 0.1% each.
Table 4
ANOVA results for the average surface roughness and tool wear
Model Parameter
|
Surface roughness
|
Tool wear
|
F-value
|
p-value
|
PCR %
|
F-value
|
p-value
|
PCR %
|
Cutting fluid concentration
|
2.98
|
0.0987
|
0.9
|
5.33
|
0.0313
|
0.1
|
Cutting speed
|
27.91
|
< 0.0001
|
8.4
|
7140.16
|
< 0.0001
|
84.8
|
Feed rate
|
145.23
|
< 0.0001
|
43.6
|
8.38
|
0.0087
|
0.1
|
Tool type
|
68.18
|
< 0.0001
|
40.9
|
622.32
|
< 0.0001
|
14.8
|
3.1 Analysis of surface roughness
Figure 1 shows the effect of fluid concentration, cutting speed and feed rate on the surface roughness of the machined components for different tool types using linear models as suggested by the RSM. Cutting fluid concentration was found to have a marginal impact on surface roughness irrespective of the employed cutting tool. Regardless of the tool type, surface roughness increased consistently with increasing feed rate and decreasing cutting speed. However, the feed rate effect was shown to be more considerable, confirming the ANOVA results shown in Table 4. Increasing the feed rate from 0.1 to 0.2 mm/rev, at constant fluid concentration and cutting speed of 10% and 102 m/min respectively, and using H10A cutting tool, caused the surface roughness to rise from 1.03 to 1.86 µm. Increased feed rate did not secure sufficient time for the cutting fluid to carry away the heat from the machining zone, leading to high material removal rate but an accumulation of chips in the tool-workpiece zone, resulting in higher surface roughness.
On the other hand, increasing cutting speed from 58 to 146 m/min, at constant fluid concentration and feed rate of 10% and 0.15 mm/rev respectively, and using H10A cutting tool, resulted in a marginal drop of the surface roughness from 1.52 to 1.35 µm. This could be attributed to the higher cutting temperature that helps soften the workpiece material and minimises the cutting forces, leading to lower surface roughness. These findings coincide with Che-Haron et al. [33] for cutting Ti-6Al-4V, where the lower surface roughness was attained at higher cutting speeds. However, it is perceived that cutting speed should be controlled at an optimal level, as the impact of high cutting temperature would conspicuously influence the tool life, cutting force, chip formation and surface finish. Finally, the type of tool material was also significant. The lowest Ra was always associated with tool type H13A for the same fluid concentration, cutting speed and feed rate
3.2 Analysis of tool wear
The effect of the three numeric process parameters (fluid concentration, cutting speed and feed rate) on the tool wear is shown in Fig. 2 (a) to (c). Tool wear was found to have a linear function of the three parameters. Nevertheless, the main numeric factor that was found imposing the most significant effect on the tool wear was the cutting speed, and the relationship was positive. The cutting tool type was also found to influence tool wear considerably and H13A had the lowest tool wear. H13A outperformed the other tool materials in terms of both tool wear and Ra owing to its superior combination of high hot hardness, high toughness, and high transverse rupture strength properties [34]. Higher cutting fluid concentration was also found to increase tool wear with only a few microns marginally.
3.3 Optimisation of process parameters
According to the results detailed in Sect. 3.1 and 3.2, it can be seen that surface roughness and tool wear vary with the assessed parameters to different extents. Therefore, an optimisation study was carried out to explore the optimum setting of machining parameters. The desirable surface finish of the machined component can be achieved while prolonging the tool life. The objective function was set to minimise both the surface roughness and tool wear. The experimental data were analysed by design-expert software, and the genetic algorithm was used to predict the process parameters based on the set objective function. The response equations describing surface roughness and tool wear in terms of the critical process parameters (showed in Eq. (2)) and the related coefficients listed in Tables 2 and 3) were solved simultaneously.
Figure 3 shows the contour plot for the optimisation function to obtain minimum values for surface roughness and tool wear for a range of fluid concentrations and cutting speeds. The model suggested that the best parameters setting to minimise average surface roughness and tool wear were 5%, 58 m/min, 0.1 mm/rev for cutting fluid concertation, cutting speed and feed rate, using the tool type H13A. At this setting, the surface roughness and tool wear are predicted to be 0.48 µm and 30 µm.
3.4 Confirmation tests and the development of surface roughness and tool wear
To validate the results predicted by the design-expert for the optimal levels of machining parameters, additional three machining trials were carried out using 5% cutting fluid concertation, 58 m/min cutting speed, 0.1 mm/rev feed rate and H13A cutting tool (suggested optimised parameters for minimum surface roughness and tool wear). Table 5 shows the measured values of surface roughness and tool wear. As shown, the average values of the three samples' surface roughness and tool wear were 0.52 µm and 30 µm, respectively.
Table 5
Results of confirmation experiments
Experiment
|
Surface Roughness (µm)
|
Tool Wear (µm)
|
1
|
0.52
|
30
|
2
|
0.51
|
29
|
3
|
0.54
|
31
|
Av.
|
0.52
|
30
|
According to the confirmation tests, good agreement was found between the predicted and experimental values. The experimental results confirmed the applied RSM technique's validity for improving the machining performance and optimising the operating parameters.
Following the confirmation test, the progress of average surfaces roughness (Ra) and tool wear was evaluated as a function of cutting distance at the optimised fluid concentration, feed rate and tool type of 5% 0.1 mm/rev and H13A tool, with different cutting speeds. Tool life tests were also conducted at the same conditions. Figure 4 shows the progression of average surfaces roughness (Ra) with cutting distance at different cutting speeds. Generally, Ra ranged from 0.49 to 1.15 µm with the cutting length for different cutting speeds. This span was found lower and narrower than a corresponding Ra progression range of 0.8–2.5 µm achieved recently in Nath et al. [35] when 1.5 l/min conventional cutting fluid was flooded during turning Ti-6Al-4V using uncoated microcrystalline carbide tool. The surface roughness at the first stage (up to 240 mm) was independent of the cutting speed. After that, sharp increase in Ra was recorded at the higher cutting speed (146 m/min) with prolonging the cutting distance of up to 600 mm. This could be attributed to the precipitous tool wear due to the rise in temperature at the cutting zone. On the other hand, surface roughness values at the lower cutting speed (58 m/min) were found steadier. This tended to retain the geometry of the tool cutting edge for a more extended period. Figure 5 shows tool edge wear for the three tools used in this study.
3.5 Tool life test
Trials at the three cutting speeds were undertaken to perform extended tool life analysis. Tool life tests were accomplished at the optimised setting (0.1 mm/rev feed rate, 5% concentration ratio and H13A tool type). Tool rejection criteria were determined following ISO standards 3685 and 8688-2 for tool life testing. The machining test was ceased if one or a combination of the following took place: maximum tool flank wear (VBB max of 0.3 mm), excessive chipping (i.e. flaking) or catastrophic fracture of the cutting edge. Tool life can be estimated with the relation:
![](https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1617643671.jpg)
were CD is total Cutting Distance to reach flank wear criterion of 0.3 mm and Fm is feed rate in mm/min [37]. Figure 8 illustrates the comparison of tool life at cutting speeds tested. Optimum tool life of 12.13 minutes was associated with the least cutting speed of 58 m/min. This could be attributed to the reduction in temperature at the machining zone, which tended to preserve the insert tip's geometry for extended periods. Further, an argument could be made that if tool wear is of higher importance to the manufacturer than the surface roughness of the sample, a lower cutting speed could be used. However, this is unlikely as titanium alloys are often used for high precision parts where the quality, including surface finish, is paramount. In addition, the graph shows a dramatic drop in tool life at cutting speeds of 91 and 146 m/min. This indicates that the cutting speed has the most dominant effect on tool life regardless of the other process parameters used (i.e. feed rate, fluid concentration and tool type).
3.6. Analysis of micro-hardness results
Micro-hardness tests were also performed at optimised cutting conditions and at the lowest and highest cutting speeds of 58 and 146 m/min. Figure 9 shows the results of the micro-hardness measurements for 58 m/min cutting speed as a function of the distance below the machined surface (starting from 30 µm), where the dashed line stands for the nominal micro-hardness of the base material before the turning process. A notable increase in micro-hardness values was found near the surface (i.e., 330 HV at the beginning of the test, at 120 mm cutting distance, and 366 HV at the end of the test cutting 1080 mm). The micro-hardness was gradually reduced towards the specimen's interior until reaching nearly the base material nominal hardness (i.e., 297 HV). This could be attributed to the plastic deformation resulting from the cutting stresses. When cutting temperature increases, there is a greater tendency for plastic deformation of subsequent workpiece layer and hence increased micro-hardness [30]. It was suggested in an investigation by [31] that a hardening effect is usually occurred during the cutting process, most probably due to the high compressive stresses at the cutting edge. Additionally, abrupt heating and cooling might have contributed to the work hardening effect during machining [32]. A noticeable increment in the micro-hardness was observed when comparing the values obtained after the first and final cuts (330 and 366 HV, respectively) [33].
Figure 10 shows the micro-hardness results for the first and last cut at 146 m/min cutting speed. In general, micro-hardness dropped from 376 HV to 297 HV in the base metal at the end of the test (600 mm cutting length), while a drop from 350 HV to 270 HV was found at the beginning of the cutting test. It was noted that these values were within the acceptable hardness range for Ti-6Al-4V aerospace parts (i.e. 419.6 HV max and 284.4 HV min). The use of a worn tool is anticipated to increase the cutting temperature due to heat accumulation at the tooltip, leading to an increase in the work hardening effect during the machining process. However, the material below the top layer of the machined surface was softer, which might be attributed to the high-temperature and tempering effect at the cutting interface when turning Ti-6Al-4V [26].
Figure 11 shows micro-hardness results after different cutting distances in all investigated conditions at two different cutting speeds of 58 m/min and 146 m/min. Similarly, a rise of micro-hardness values with increased the cutting distance was seen. However, the highest micro-hardness measured was 376 HV when machining at the higher cutting speed of 146 m/min after the uncoated carbide H13A tool has failed. In contrast, at the lower cutting speed, a micro-hardness of 366 HV was recorded. It was also observed that when longer cutting was carried out with higher flank wear, the machined surface's disturbed layer's hardness increased significantly under all cutting conditions.