Tab. 3 and Fig. 3 present the roughness values generated in end milling under different lubricooling conditions. Each Ra and Rz value obtained represents the arithmetic mean of three measurements performed in the stable region of the machined surface (disregarding the sections corresponding to the entrance and exit of the cutter) in each sample. Besides, runs 3*, 8*, and 13* refer to the controllable input variables at the central point that allows trends to be evaluated during the process. The results show that, on average, each lubricooling method provided different ISO categories of surface finish. Dry (Ra = 0.433 μm) presented class N6 (0.4 < Ra £ 0.8 μm), flood (Ra = 0.287 μm) generated class N5 (0.2 < Ra £ 0.4 μm) and NF-RQL (Ra = 0.183 μm) resulted in class N4 (0.1 < Ra £ 0.2 μm). According to Kalpakjian and Schmid [26], classes N4 and N5 in milling can be considered fine finishing processes, while class N6 approaches common roughness values.
The direct comparison of results obtained in the experiments shows a significant gain in using NF-RQL over the others, resulting in lower values of Ra and Rz in most runs. The average reduction of Ra values was 51.6% compared to dry milling and 31.2% to flood milling. For Rz values, the decreases were 43.6% and 30.7%, respectively. Sharma et al. [27] explained that fluids with the addition of nanoparticles have better tribological properties than fluids without them, which is evidenced in the roughness obtained under NF-RQL.
Run 14, followed by run 1, showed the highest Ra and Rz roughness values under all lubricooling techniques. Run 14 used the higher levels of ap and fz, combined with middle vc, while run 1 employed the higher levels of ap and vc, associated with middle fz. Since there is no simultaneous combination of three controllable factors in the highest condition, these runs can be considered the most severe conditions of the experiment. Besides, run 14 generated the highest roughness in dry milling because it combines the critical combination with the absence of a lubricooling environment, thus justifying the high values obtained of Ra and Rz.
Alternatively, runs 7 and 15 had the lowest roughness values. In these runs, despite vc7 = 80 m/min and vc15 = 120 m/min, both used the low level of fz and middle ap. According to Klocke [5], the situation ap = re guarantees better stability for the end milling, with slighter variations in the cutting force that result in a more uniform machined surface. Notably, run 7 with flood and NF-RQL produced the lowest surface roughness without significant differences between the values obtained under both lubricooling conditions, probably due to the high significance of feed per tooth in milling hardened steels. According to Chinchanikar et al. [28], the lower fz results in minor cutting force and slight surface roughness. Another significant factor is ap and its interaction with fz, which present ideal combinations according to cutting parameters used, while vc does not contribute significantly [29]. This result explains the similarity of the results obtained with run 15 since the other parameters are identical.
Aiming to verify statistically whether any random variables affected the process stability and the machined surface finish, Tab. 4 shows the variance (s2) of the nine values of Ra and Rz measured in the runs performed at the central points. The higher variation of Rz shows that this parameter is more sensitive to random variables intrinsic to the process (chip formation, cutting temperature, tool wear, among others). Furthermore, NF-RQL was more stable for Ra and flood condition for Rz. Then, the results show that the experiments occurred under stable conditions.
Fig. 4 shows the roughness profile and surface image produced by run 7 (lower roughness values) in each lubricooling method. The differences were evidenced. According to Wojciechowski et al. [30], the surface profiles have well-defined peaks and valleys when the feed movement of the cutting tool in the milling process is stable. However, these profiles can be affected by possible difficulties in the chip shearing. These difficulties may have influenced the surface profile under flood milling in plastically deforming the material due to its work-hardening rate. The flood condition tends to generate lower temperatures in the cutting zone than the other conditions, resulting in significant variations in its texture. The profile obtained by dry milling has a well-defined distribution but presents greater amplitudes of peaks and valleys than the others. The profile pattern obtained under the NF-RQL is similar to the dry condition but with a lower amplitude. Majerik and Barenyi [31] point out that temperatures above 250 ºC can cause microstructural changes in Hardox and degradation in mechanical properties (mainly tensile strength and hardness). In the cooling conditions, plastic deformation may have been facilitated, improving the surface finish by machining. Therefore, NF-RQL offers better tribological characteristics than the other conditions.
Fig. 5 shows the statistical analysis of the Abbott-Firestone Curve (AFC) and Amplitude-Probability Distribution (APD) for the three lubricooling strategies in run 7. NF-RQL presents good performance since the AFC has a lower slope than the others, in addition to an APD with a higher concentration of values around the mean, i.e., leptokurtic distribution (positive kurtosis). This result indicates that the profile has low roughness values and slight variation in its peaks and valleys compared to other lubricooling environments, which can be highly desirable in several industrial applications. However, the situation with the most significant variation was dry milling, in which the AFC displayed greater inclination and the APD considerable dispersion, i.e., a platykurtic distribution (negative kurtosis). Sharma et al. [32] explain that the coefficient of friction produced by dry milling is significantly higher than when using cutting fluid, resulting in higher cutting forces and roughness values. Thus, the effects of lubricating and cooling conditions on the surface finish tend to be positive, which justifies the similarity of the under Flood and NF-RQL results in specific runs.
Aiming to investigate the controllable factors and their interactions, the analysis of variance (ANOVA) was applied, allowing the evaluation of their effects on each response variable. Input factors with a p-value £ 0.05 (95% confidence interval) were taken into account as significant parameters, while a 0.05 < p-value £ 0.10 (confidence interval between 90 and 95%) were considered partially significant. Tab. 4 presents the p-values obtained for Ra values under each cutting condition, followed by their respective contribution. Analogously, Tab. 5 shows the ANOVA for Rz.
In Tab. 4, the linear effect of feed per tooth (fz) is the most significant parameter on the average surface roughness (Ra) under all lubricooling conditions. It was also verified that the cutting speed (vc) and its interactions are not significant. This result can be observed by comparing runs 2 and 10 and between runs 7 and 15, in which there are no significant differences in the Ra values with the increment of vc for the same feed per tooth (fz) values and axial depth of cut (ap). When NF-RQL and flood are applied in the milling of HardoxÒ 450 the linear and quadratic effects of ap become significant. Besides, the interaction fz x ap partially affects Ra only in the dry condition. The contribution of ap may be related to the reduction of the friction coefficient due to the formation of an oxide film layer at the tool/machined surface interface provided by the fluid [32]. Thus, the influence of fz is reduced with this tribological improvement, spreading with the increase of ap. Another possible explanation is the reduced hardness of the machined material due to the higher temperature in the cutting zone in dry machining, which favors the chip formation, making the ap less significant for the process [22]. Alternatively, fluid-assisted milling seeks to keep the lower temperature in the cutting zone, resulting in the conservation and increase of hardness during the chip shearing, representing the significant contribution of ap to the surface roughness [33].
In Tab. 5, the linear effects of fz and ap are the factors most significantly affecting Rz in all lubricooling techniques evaluated. However, the quadratic effect of ap is significant only when NF-RQL or flood is applied. Again, the interaction fz x ap partially affects Rz solely in the dry condition. The results obtained for Rz are similar to those obtained for Ra since the main factors that affect the significance of ap in dry milling compared with NF-RQL or flood milling are possibly due to the same phenomena. Unlike Ra, the parameter Rz is sensitive to vc in the NF-RQL condition, in which the interaction vc x ap was significant; in this case, such a change may be related to the greater sensitivity of Rz in the detection of roughness peaks since this condition generates a better finish compared to the other lubricooling states, making slight variations between peaks and valleys more representative to Rz parameter [17]. According to Hsu et al. [33], the increase in vc can influence the roughness and temperatures in the machining region. However, Dixit et al. [10] mention that in techniques such as MQL and RQL, the high temperatures can cause fluid vaporization, influencing the quality of the lubricooling effect and the machined surface finish.
The empirical model representing the two responses (Y) can be described as functions of controllable input factors (vc, ap, fz) and expressed as in Eq. (1).
![](data:image/png;base64,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)
The coefficient values for the estimation models of Ra and Rz under different lubricooling environments are shown in Tab. 6. The comparison plots of the experimental results obtained compared with those predicted through the model are shown in Fig. 6. The excellent approximation of the values estimated by the model for those measured experimentally demonstrates the ability of the respective second-order polynomials to predict the results since all determination coefficients (R2) stood above 85%.
Fig. 7 shows the contour plots for each lubricooling condition considering the lowest level of feed per tooth (fz = 0.05 mm/tooth), found by ANOVA as the most significant parameter and showing higher surface quality as the lower its value. In dry cutting, the lowest Ra and Rz values are obtained with cutting speed (vc) at a low level (80 m/min) and axial depth of cut (ap) close to 1.0 mm. For NF-RQL, the region of lower Ra is divided into two zones, one with medium/high ap and low vc (i.e., for ap = 1.2 mm, vc £ 85 m/min; for ap = 0.8 mm, vc £ 102.7 m/min) while the other appears with high vc (120 m/min) and low ap (0.4 mm). In the case of Rz, the lowest values are found in a region that encompasses the parameter ranges for the lowest Ra; however, the range is less wide-ranging for Rz. For the flood condition, the contour curves tend to present a pattern for the smallest Ra values from the approximate relationship vc < 240 – 200×ap, indicating little influence of the variation of vc on the process, while the lowest Ra is observed with ap < 0.63 mm for any vc. Again, the smallest Rz are in a region that covers the parameter ranges for the slightest Ra values. Due to the pattern observed in several studies on the milling of hardened materials, this trend is expected. Whereas, under different lubricooling environments, the main factors that affect the surface finish are fz and ap, with vc having little influence and significant contributions only in exceptional cases [33], [34], [35].
Based on ANOVA and contour plots that present the conditions that provide better surface finishes, it is noticed that there is a correspondence between the Ra and Rz behavior in regions with lower roughness values. This behavior suggests the possibility of simultaneously reaching the lowest values for such parameters. Thus, a multi-objective optimization for roughness was performed. Fig. 8 presents the composite desirability function for multi-response optimization results for dry, NF-RQL, and flood conditions. For all lubricooling strategies, the composite desirability (D) equal to 1.0 was reached. According to Ferreira et al. [25], D = 1.0 presents the optimal maximum or minimum, resulting from the geometric mean of the individual desirability (d) of each analysis parameter. The summarized results are shown in Tab. 7.
The values of Ra and Rz obtained using the multivariate optimization are slightly smaller than those obtained with run 7, which presents a similar combination of parameters, changing only the ap level (0.8 mm). Such results are consistent with ANOVA and contour plots parameterized for fz = 0.05 mm/tooth. When comparing the optimized parameters for NF-RQL and flood, in addition to the ecological aspects associated with fluid disposal, the material removal rate (MRR) is higher for NF-RQL (2315 mm3/min) compared to flood (1266 mm3/min) for similar roughness values. In the case of economic aspects, other factors need to be evaluated, such as the cost and consumption of NF-MLG in RQL compared with BD-Fluid B90 in abundance.