1 Introduction

MQL is the abbreviation of minimum quantity lubrication or simply micro lubrication near dry machining, which aims to reduce hazardous environmental and health conditions during machining operations. However, there is not yet an exact and standardized definition of MQL. The use of an average of not more than 50 ml/h of lubricant is the definition of minimum quantity lubrication [1]. Referring to high machining expenses associated with the use of cutting fluids and their detrimental influences on the operator health and environmental pollutions, alternative methods were always demanded [2]. To remedy the difficulties abovementioned, MQL was proposed, and it became an exciting and popular method in machining industries. As a result of MQL machining, the thermal shock of the cutting tool is reduced. This may lead to improved tool life and performance [3]. This implies that the operated tool and nozzles model and location, as well as the mounting strategy, must be selected precisely. In general, the nozzles must be located within 2.5–5 mm away from the cutting zone [4]. The high-pressure jet of lubricant into the chip-tool interface may decrease the cutting temperature. Consequently, it may lead to a prolonged tool life [5]. However, the main drawbacks of MQL are the inability of complete heat transfer and chip evacuations, which are considered the main reasons for corrosion in the work parts, which also tends to affect the machinability of the tested materials. Comprehensive investigations on the effects of MQL on machinability attributes are then strongly required.

The effects of lubrication modes, in principle MQL on various aspects of machining and machinability attributes of AAs, were reported in numerous work [6,7,8,9,10,11,12]. For instance, as noted in [6], proper lubrication is highly related to the type of lubricant used. However, despite using lubricants with excellent quality, the tool damage cannot be avoided. Damir et al. [7] denoted that the amount of coolant determines the level of material adhesion to the tool surface, and the MQL may not certainly reduce the tool wear. Furthermore, a direct relationship could be formulated between the cooling application system and the recorded cutting forces.

Vikram Kumar et al. [8] studied the hard turning of AISI 4340 alloy steel in dry, MQL, and wet conditions. Although the better surface quality resulted under MQL than wet and dry conditions, however, referring to the narrow range of feed rate used (0.04–0.06 mm/rev), no relationship can be established between roughness and feed rate. Another study was conducted on the hard turning of AISI 4340 alloy steel under dry, MQL, and wet conditions [9]. Within the feed rate range of 0.05–0.14 mm/rev and cutting speed 120 m/min, the roughness was approximately similar and constant under different lubrication conditions when the feed rate was within the range of 0.05–0.1 mm/rev. According to Ozawa et al. [10], MQL yields to good surface roughness results. The chip formation under dry, MQL, and wet turning of AISI 1040 was studied by Dhar et al. [11]. The feed rate and cutting speed ranges were 0.1–0.2 mm/rev and 60–130 m/min, respectively. As noted in [11], despite the feed rate used, the chip reduction coefficient decreases when the cutting speed increases and the lowest chip reduction coefficient values were obtained under MQL condition. Yoshimura et al. [12] studied the tool wear modes under MQL machining of aluminum alloy. It was observed that the amount of adhered material is reduced when the cutting speed increases.

Among machinability attributes, special attention is paid to surface quality after machining operation. As noted earlier, among the surface quality attributes, the average surface roughness (Ra) is considered the main parameter, which represents the random and repetitive deviations of a surface profile from the nominal surface [13]. The surface roughness is generally determined by Eq. (1) as follows:

$$ {R}_{\mathrm{a}}=\raisebox{1ex}{${f}^2$}\!\left/ \!\raisebox{-1ex}{$32r$}\right. $$
(1)

where f is the feed rate, and r denotes the nose radius.

It is agreed that adequate surface roughness can be achieved with reduced friction, wear, and noise, as well as improved corrosion resistance [14]. The effects of the abovementioned cutting parameters on the surface roughness were studied in numerous experimental studies [11, 15,16,17,18,19,20,21]. However, the impact of multiple machining parameters such as tool geometry, machine tool rigidity, lubrication modes, lubrication flow rate, and vibration is not yet incorporated into Eq. (1) [22]. Furthermore, to obtain adequate surface quality as well as multiple response optimization, optimum process parameters were proposed using sophisticated optimization tools [23].

To the authors’ knowledge, limited studies were found on the factors governing surface quality, in principle, average surface roughness (Ra), and chip thickness (hc) in turning of AA 7075-T6 and AA 6061-T6 when various types of lubricants and different levels of flow rate are used. The adequate selection of cutting parameters to guarantee acceptable surface quality may reduce the needs of protracted deburring and edge finishing processes, which are associated with additional non-desirable expenses and harmful effects on environments and operator’s health, aligned with green machining [6]. In general, within most of the reported research works on MQL, vegetable oil is the prime choice of lubricant. A low amount of works has considered the effects of various flow rates on machining outputs. In order to remedy the lack of knowledge abovementioned, a parametric design of an experiment based on multilevel factorial was used to determine the influence of cutting parameters, lubricants, and flow rates on the Ra and hc when turning two aero-engine aluminum alloy 6061-T6 and 7075-T6 under micro lubricated condition (MQL). The statistical tools, including ANOVA, were also used as a supportive tool for statistical analysis.

2 Experimental procedure

The turning tests were conducted on the cylindrical aluminum alloy 6061-T6 and 7076-T6 (Ø150 × 450 mm) using different levels of cutting parameters (Table 1). It was intended to use similar levels of experimental parameters used in the world-class industrial sectors. Therefore, to cover the wide range of cutting capabilities, the experimental conditions, as presented in Table 1, were selected according to industrial recommendations under MQL. To that end, a multilevel full factorial design of experiments, including 180 tests (5 × 6 × 3 × 2), was used for each material (Table 1). In total, 720 tests were conducted, including one replication for each test. The experimental tests were completed on the CNC machine (Mazak Quick Turn Nexus 100 II M). The average value of responses was considered for results analysis. The new carbide cutting insert (DNGP-432 KC5410 Kennametal) was used in each test. Figure 1 depicts the experimental setup, equipped with an MQL system. The surface roughness of generated surfaces was evaluated using the Mitutoyo SJ 400 profilometer (Fig. 1c). The surface roughness was recorded at four different positions (90°apart), and the measurements were repeated twice at each point. Five surface roughness parameters, including Ra, Rq, Rt, Rv, and Rp, were recorded. However, the results of Ra were only used for additional analysis. A detailed overview of the effects of cutting parameters on other surface quality attributes is within the scope of further study. The Hitachi scanning electron microscope (SEM) S-3600N, as shown in Fig. 1d, was used to capture the high-resolution images of the chips. The samples were ultrasonically cleaned in ethanol bath before being transferred to the SEM machine. The burr formation morphology was also monitored using a high-resolution optical microscope.

Table 1 Experimental process parameters and tools
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Fig. 1
figure 1

Experimental setup. a Microlub system setup. b Arrangement of MQL nozzles. c Mitutoyo Surface profilometer. d Hitachi scanning electron microscope (SEM) S-3600N

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The operated micro lubrication system, as depicted in Fig. 1, consisted of a volumetric micropump that injects a low volume of lubricant through a capillary tube to an outlet nozzle (Fig. 1b). Simultaneously, a low-pressure pulverization air was injected into the cutting zone using a second capillary tube. The lubricant source is installed at the top of this machine, and the flow rate could be adjusted by setting the micropumps which are pulsed either by a pneumatic sequencer that allows set up from 1 to 180 strokes per minute [24]. The adjustments of the micro lubrication system are shown in Table 2. The specifications of two lubricants proposed by System Tecnolub Inc. are shown in Table 5 (Annex).

Table 2 Adjustment of Microlub system
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3 Results

3.1 Method of analysis

It is believed that adequate selection of cutting fluid and flow rate leads to significant improvement in the lubrication performance and adequate machining expenses are expected. As noted earlier, it is believed that Ra is one of the critical surface quality attributes which is affected by different cutting parameters including work material, tool geometry, cutting conditions, and lubrication strategy [25]. To determine the effects of cutting parameters, including feed rate, cutting speed, flow rate, and lubricant on both Ra and hc, different statistical methods such as Pareto analysis, main effect plot, and analysis of variance (ANOVA) were used. In addition, the results were presented in various design models, mainly known as linear, 2-factor interactions, and multiplicative models. A complete overview of the statistical parameters used is shown in [26].

3.2 General analysis

In the first step, to evaluate the effects of material properties on the machining outputs, all obtained results were analyzed using statistical tools. According to Fig. 2, it can be stated that although different materials with completely different mechanical properties were used [27], it can be however observed that variation of surface roughness values mainly depends on the feed rate and cutting speed, nor material properties. This can be related to the mechanism of machining operation, the chip formation morphology, and the effects of lubrication conditions. However, different results are expected in the case of milling operations where surface roughness may be widely affected due to progressive chip formation [26].

Fig. 2
figure 2

Pareto chart of average surface roughness Ra (R2 = 0.915; R2adj = 0.911)

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3.3 Individual material analysis

In the second step, the individual analysis was conducted concerning each material. According to statistical analysis, the correlation of determination R2 and R2adj of Ra and hc (Table 3) denote that except linear model of Ra, it can be exhibited that despite the design model used, design models of machining responses are statistically significant (R2 > 0.8 and P value < 0.05) concerning the variation of process parameters used. The negligible difference between R2 and R2adj in linear and quadratic models in both responses denotes the non-significant influence of interaction effects between cutting process parameters. Therefore, the linear design model was used in the following analysis as the primary statistical significant model. The difference between R2 and R2adj in linear and quadratic models of Ra and hc is about 10%. Moreover, the P value of 0 in linear and 2-factor interaction models of both Ra and hc indicates a negligible contribution of interactive effects on the presented results. Therefore, as well as the first part of this experimental study, the linear design model is used for further analysis.

Table 3 ANOVA table of average surface roughness and chip thickness
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Figure 6 a and b show that feed rate (A) has the most significant effect on Ra while cutting speed (B) and lubricant (D) have negligible effects on it. According to Fig. 7b, an increased feed rate leads to a more deteriorated surface quality. This could be attributed to the direct influence of feed rate on hc and directional cutting forces, which cause severe deviations on the surface texture and profile [25, 28]. When the cutting parameters listed in Table 1 are used, the model as fitted has the capability to control the variability of Ra up to 82.08%. Figure 7 a and b denote that feed rate (A) and cutting speed (B) have the most significant effects on the hc. Increased cutting speed leads to decreased hc, and inversely, increased feed rate led to thicker chips. The correlation of determination R2 indicates that under similar experimental conditions as presented in Table 1, the hc can be controlled up to 93.58% when using the linear design model. It can be observed that lubricant has an insignificant effect on Ra and hc. This can be related to the intense impacts of flow rate on the generated temperature in the cutting zone as well as friction, which both tend to be reduced at higher levels of flow rate.

The regression models between Ra and hc in linear, exponential, and multiplicative models are shown in Table 4. A statistically significant relationship exists between Ra and hc at the 95.0% confidence level. A negligible difference can be observed between the correlation coefficients of both linear and multiplicative models. According to Table 4, it can be exhibited that despite regression models presented, the Ra and hc are strongly correlated with each other. According to Eq. (2), knowing that the chip thickness is directly formulated as a function of feed rate, therefore, increased feed rate and chip thickness lead to a higher chip thickness ratio, which itself may tend to increase the shear angle and decrease the friction angle, as shown in Eqs. [3,4,5], respectively [29]. Friction forces, to a large extent, affected by friction angle [30]. Therefore, as shown in Eq. (6), lower friction force has resulted when the feed rate increases. This tends to generate an excellent surface finish.

$$ {r}_{\mathrm{c}}=\frac{h}{h_{\mathrm{c}}} $$
(2)
$$ {\varphi}_{\mathrm{c}}={\tan}^{-1}\frac{r_{\mathrm{c}}\cos {\alpha}_{\mathrm{r}}}{1-{r}_{\mathrm{c}}\sin {\alpha}_{\mathrm{r}}} $$
(3)
$$ {\varphi}_{\mathrm{c}}=\frac{\pi }{4}\left({\beta}_{\upalpha}-{\alpha}_{\mathrm{r}}\right) $$
(4)
$$ {\mu}_{\upalpha}=\tan {\beta}_{\upalpha} $$
(5)
$$ {\mu}_{\upalpha}=\frac{F_{\mathrm{u}}}{F_{\mathrm{v}}} $$
(6)
Table 4 Statistical results of regression models between chip thickness and surface roughness
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Referring to Table 4, higher values of R2 were found for regression models of Ra and hc in AA7075-T6 than those observed for AA6061-T6. In other words, Ra and hc in AA7075-T6 are more controllable under the variation of cutting parameters as compared with AA6061-T6. Furthermore, the differences between the resulted values of R2 and R2adj of design models in both materials can be attributed to the difference between governing factors on the Ra and hc, which were discussed in Figs. 2, 3, 4, 5, 6, and 7. Therefore, despite the design model used, better regression was found between Ra and hc in AA7075-T6 than AA6061-T6 (Figs. 8 and 9).

Fig. 3
figure 3

Main effect plot of average surface roughness Ra

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Fig. 4
figure 4

Pareto chart of chip thickness (R2 = 0.974; R2adj = 0.973)

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Fig. 5
figure 5

Main effect plot of chip thickness hc

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Fig. 6
figure 6

Pareto chart of Ra in linear design model. a AA 6061-T6. b AA 7075-T6

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Fig. 7
figure 7

Pareto chart of hc in linear design model. a AA 6061-T6. b AA 7075-T6

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Fig. 8
figure 8

Linear regression model of average surface roughness and chip thickness in AA 6601-T6

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Fig. 9
figure 9

Linear regression model of average surface roughness and chip thickness in AA 7075-T6

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4 Conclusion

Following experimental studies and statistical analysis presented, the following conclusion can be drawn with respect to operating conditions used:

  • Despite three different experimental models, including multiplicative, 2-factor interactions (2FI) as well as linear models used, both Ra and hc are statistically significant responses and could be controlled by variation of cutting parameters used. A strong relationship can be formulated between both responses and experimental parameters used.

  • It can be observed that feed rate has a significant effect on Ra and hc while cutting speed has just the considerable impact on hc. The effects of feed rate can be attributed to powerful influences on the friction and chip thickness, which may lead to increased levels of temperature in the cutting zone and fluctuated force, which all lead to diminished surface quality.

  • According to experimental observations, although negligible, however biodegradable cutting fluids with higher viscosity denoted better capability to improve the surface finish. The use of a higher flow rate also led to improved surface finish (up to 50%). It was observed that both the flow rate and cutting fluid have insignificant effects on hc.

  • Significant correlations were found between Ra and hc in linear, multiplicative, and exponential models under different levels of flow rate.