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Experimental investigation into machining performance of magnesium alloy AZ91D under dry, minimum quantity lubrication, and nano minimum quantity lubrication environments

  • Ajay Kumar , Simranpreet Singh Gill , Gurpreet Singh , Shubham Sharma EMAIL logo , Shashi Prakash Dwivedi , Kahtan A. Mohammed , Kuldeep Sharma , Dražan Kozak , Anica Hunjet EMAIL logo and Mohamed Abbas
Published/Copyright: June 24, 2024
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 43 Issue 1

Abstract

Due to its low density, magnesium is recognized as a lighter metal and it is favorable for frequent use in industries. It is used in aerospace, biomedical, automotive, and other industrial applications. Magnesium is a promising element that is vital for reducing emissions, improving efficiency, protecting the environment, and enhancing the machine economy. This study analyzes the influence of various cutting environments and parameters on the turning operation of magnesium base alloy (AZ91D). Aluminum 9% and Zinc 1% is the main constituent of AZ91D. The machining process was accomplished using dry, minimum quantity lubrication (MQL), and nano minimum quantity lubrication (NMQL) environments based on their influence on surface roughness (SR) and temperature. Under certain circumstances, it was observed that SR decreases with the increase in the cutting velocity (V c), feed rate, and depth of cut. During cutting of AZ91D in dry conditions, it is preferred to use a moderate speed. Higher temperature was recorded during dry conditions which can significantly reduce the life span of the tool. MQL and NMQL have reduced the cutting temperature by a margin of 25–40% compared to dry machining, thus improving tool life. NMQL has shown decent cooling results compared to other cooling systems.

Keywords: dry machining; minimum quantity lubrication; nano-MQL; RSM; ANOVA; turning

1 Introduction

The aim of this study is to identify the effect of molybdenum disulfate (MoS2) base nanoparticles during machining of magnesium alloy. The need to reduce energy usage is commonly acknowledged considering the lack of resources and the rise in environmental degradation. Magnesium has the highest possibility for reducing weight [1], fuel consumption, and greenhouse gas effects [2]. When magnesium is in its molten condition and is exposed to oxygen, it starts to ignite. During cutting operations, the main issue is the possibility of fire. Magnesium alloys are best cut with high-speed dry cutting since it eliminates the need for additional component cleaning. Also, the labor is more environmentally friendly and thus, address ecological problems is a massive concern [3]. Aluminum and magnesium alloys’ excellent strength-to-mass ratio gives automotive and aerospace engineers the chance to utilize these lightweight replacements in lieu of the traditional steel and cast-iron components used in the transportation sector. Cast aluminum and magnesium alloy automotive engine components efficiently reduce fuel consumption by lowering the overall mass of each vehicle [4]. Magnesium is a commonly used industrial material because of its lightweight structure. Its alloys are recyclable, and have a low density, a high specific strength, and a high specific stiffness [5]. Due to alterations in the microstructure, particularly in the Zr-rich areas and the grain boundary T-phase (Mg7Zn3RE), the corrosion of the heat-treated alloy is considerably changed [6]. Magnesium alloy is widely employed in the fields of aircraft, automotive, electronics, and biomedicine. Due to the low melting point, built up edge (BUP) and BUL formed and the temperature was reduced by up to 15% by submerged convective cooling technique [7]. Cast Mg has excellent properties. It helps to reduce CO2 emissions in the automotive industry. This helps to reduce mass and improve corrosion resistance [8].

Flank wear of magnesium during turning has investigated the cutting force with the SR is constant up to 550 m·min−1 and then increases sharply. Further, the cutting forces increase with the length of the cut [9]. Magnesium helps vehicles run more efficiently and emit less CO2. Mg alloy assists in reducing component weight by 22–70%. Galvanic corrosion resistance increases when it is molten, causing it to become more reactive. The risk of fire is a significant difficulty while machining [10]. High-speed cutting helps to improve machining efficiency and power loss. Depth of cut (DOC) influences cutting forces; surface roughness (SR) increases with feed and DOC but decreases with speed [11]. Cutting parameter during the machining of AZ31B-O has been studied and it was observed that large feed rate at low cutting energy affects layer thickness and compressive residual stress. Cryogenic technique helps to improve mechanical and thermal properties [12]. Coated tool performance study reported that non-coated tools cause excessive tool wear [13]. The DOC has primary effect on the cutting forces during machining of AZ91D material. High-speed machining improves productivity and lowers power loss. The fractures deepen as the machining time increases and minor tipping is formed at the cutting tool edge. In cases of crumble style, cutting speed and DOC were less effective than feed rate. SR increases with higher feed rates and cut depths, while it decreases with higher speeds [14]. SR decreases when the feed rate is increased and coated tools are used. Steel-cutting equipment performs better than non-ferrous equipment.

The best machining conditions result in less SR and more economical machining [15]. SR of titanium graded improves as the speed and feed rate increase but the SR decreases with approach angle and DOC. The tangential force increases with the approach angle and DOC but may decrease with speed and feed, Response surface methodology (RSM) is reasonably accurate and can be within limit [16]. CO2 helps to increase tool life than flood cooling technique. Supercritical CO2 MQL increases material removal rate and improves lubrication than conventional cooling [17]. Reduced cutting force and tool wear improve performance by applying nanofluid. With the improved concentration of nanofluid, the conductivity, viscosity, and density also increased [18]. Nanoparticles reduced cutting force and tool wear with the increase in the cutting speed, feed rate, and rake angle [19]. The feed rate has a considerable impact on the SR. Cutting speed has little bearing on cutting depth, which is the second factor, the cutting parameters are optimized and tool life is analyzed to realize the effective and affordable cutting of difficult-to-process materials. The cutting parameters are optimized and tool life is analyzed to realize the effective and affordable cutting of difficult-to-process materials [20]. MQL promotes energy and resource conservation consequently creating a green atmosphere. The main problems encountered while cutting difficult-to-cut materials are cooling restrictions and chip evacuation capacity [21]. Liquid nitrogen reduces cutting temperature up to 60%, the hardness value of the material increases and surface integrity improves up to 45% [22]. At the lowest cutting conditions during MQL, little tool wear, cutting force, temperature, and better tool life have all been noted. The lowest and highest velocity both produced better surface finishes. Greater results than dry conditions were provided by the Taguchi Base GRA technique [23]. Surface quality was better in a cryogenic environment than in a dry one [24]. Cutting temperature chip reduction coefficient and SR are both reduced by MQL. The MQL contributes to preserving ecological equilibrium [25]. From literature review, it has been observed that there are only limited works on the machining of AZ91 under different cooling conditions along with variant range of process parameters. Hence, there was a research gap that must be filled in by exploring the machining characteristics of magnesium-based AZ91D alloy. Therefore, in the present investigation, the impact of dry, MQL, and NMQL conditions on temperature and surface finish during turning has been investigated.

Generally, today’s industry focuses on higher productivity without costs to the environment. Also, magnesium is favorable due to its lowest density and melting point [26,27,28]. It is difficult to machine this material at higher velocity because of flaming. Furthermore, in the above literature, it was observed that less focus on the effect of MoS2 base nano-fluid on the machining of AZ91D. This article tries to give an appropriate approach to finding the range of cutting parameters and suitable machining environments for mentioned material under variant range of process parameter. Along with this, different environments such as dry, vegetable oil MQL, and nano-MoS2 mixed vegetable oil have been tested for increasing the production rate.

2 Materials and methodology

2.1 Material used

The “AZ” stands for “Aluminum-Zinc” in AZ91D, representing the composition’s essential alloying constituents. The value “91” denotes the nominal magnesium concentration, which in this instance is 9%. The magnesium alloy is typically used in die casting, due to its greater corrosion resistance, castability, and mechanical attributes. In comparison to aluminum and zinc, AZ91D magnesium is less dense and satisfies the demands for tensile strength, yield strength, and hardness, in die casting, because of its superior corrosion resistance, castability, and mechanical attributes [29,30]. In comparison to aluminum and zinc, AZ91D magnesium is less dense and fulfills the demands for tensile strength, yield strength, and hardness. Test material AZ91D was selected for the experimental study in the form of a cylindrical specimen ϕ 90 mm × 300 mm. The composition of the material has been described in Table 1. Due to the low melting point of this alloy, it is a very challenging task to machine magnesium alloy under a conventional machining system [31] (Table 2).

Table 1

Composition of work specimen (wt%)

Al Mn Si Cu Fe Zn Ni
8.9% 0.22% 0.35% 0.01% 0.0025% 0.58% 0.0003%
Table 2

Mechanical properties of AZ91D (Guo et al., 2010)

Density (g·cm−3) Elongation (%) Hardness Tensile yield strength (MPa) Compressive yield strength (MPa) Ultimate tensile strength (MPa) Ultimate compressive strength (MPa)
1.81 3.8 64HBS 246 160 240 250

2.2 Nanoparticles

A nanoparticle is a tiny particle having a size between 1 and 100 nm. The physical and chemical characteristics of nanoparticles, which are invisible to human vision, may vary significantly in comparison to those of their larger material counterparts. Nanomaterials are used in a wide range of fields from air purification and environmental protection to healthcare and cosmetics [26,32,33]. It helps to enhance the chemical properties of the lubricant and improve the flash point of the lubricant and machining performance leading to achieving the ecological machining process. MoS2 has been selected as nanoparticles for the present research because of its excellent thermal consistency, great tendency to withstand high temperatures, produce better surface finish, and thus minimize cutting temperature [34,35,36]. On the other side, nanoparticles help to reduce friction at the tool chip interface.

2.3 Tool used

Carbide tools have a great ability to withstand higher temperatures and pressure [37,38]. Generally, it is used in metalwork, woodwork, and other manufacturing applications due to better tool life, and durability than traditional tools [39]. CVD coated carbide inserts (CNMG120408) selected for the machining purpose having geometry rake angle (α) = 0°, nose radius (γ) = 0.8 mm, clearance angle (δ) = 0°, and cutting-edge length (£) = 12.

2.4 SR measurement

Surface finish, which describes the final texture following manufacturing or machining procedures, is closely connected to SR [40,41]. Surface finishes can vary depending on the manufacturing process used, which can lead to varied SR. During machining, heat is generated between the tool and workpiece due to friction which may affect the surface texture of the workpiece and is measured with the help of a SR tester [42,43,44]. In this experiment, Handysurfe-35b is a type of SR tester that has been used for roughness measurement. During measurement, value of SR, Ra, is to be noted.

2.5 Temperature measurement

Heat generation is a common phenomenon that occurs between the workpiece and the tool during the machining process and leads to a decrease in tool life [45]. The temperature has been one biggest concern in reducing tool life. Different methodologies can be used for measuring temperature [46]. In the present investigation, an infrared thermometer has been used to measure the temperature between the tool and the workpiece. The range of the thermocouple is −50 to 580°C.

2.6 Work methodology

An experimental study has been conducted on a rigid lathe machine during the turning of a solid rod of magnesium base alloy AZ91D with a diameter of 90 mm and length of 300 mm. Four input parameters (velocity, DOC, feed rate, and environment) and two responses, SR and temperature, have been considered. All experimental conditions contain an equal distribution of workpiece length, and each time the specimen was machined, a new cutting edge was used. CNMG120408 tool was selected for the experiment. The cutting tool range has been selected according to the manufacturer’s suggested parameters. The focus of this study is to find the outcome of different parameters on SR and temperature during different environments of machining. An SR tester is used for measuring surface integrity. A lubricant flow rate of 120 ml·h−1 during MQL and NMQL is used. As per the reports of previous research, synthetic oil is not suitable for ecological balance and is not economically viable too. It may cause harmful effects on human health, is difficult to degrade, and handling costs increase. Hence, biodegradable olive oil has been selected as a lubricant during machining. It is non-toxic and helps to achieve sustainable machining [47,48]. Nanoparticles of MoS2 in powder form were added to the lubricant by 9% of weight. A sample of 250 ml liquid oil was used for mixing nanoparticles and to produce different concentrations.

The prime objective of the present research is to compare the impact of adding MoS2 base lubrication on SR and temperature to other conditions. MQL is supplied at 6 bar pressure with the help of a nozzle at the rake face near the cutting zone. The infrared thermocouple is used for measuring temperature. The experimental setup is shown in Figure 1. On the other side, a sample of olive oil with suspended MoS2 is demonstrated in Figure 2a, and the ultrasonication process for the preparation of lubrication oil is shown in Figure 2b. The details of experimentation conditions have been mentioned in Table 1 describing work material, tool used, cooling conditions, temperature, and SR measurement (Table 3).

Figure 1 Experimental setup for machining AZ91D.
Figure 1

Experimental setup for machining AZ91D.

Figure 2 (a) Olive oil with suspended nanoparticles and (b) ultra sonification process for mixing suspended nanoparticles in olive oil.
Figure 2

(a) Olive oil with suspended nanoparticles and (b) ultra sonification process for mixing suspended nanoparticles in olive oil.

Table 3

Experimental conditions and input parameters range

S. No. Item Description
1 Machine used Lathe machine
2 Work specimen Magnesium alloy AZ91D, size: ϕ 90 × 300mm
3 Cutting tool (insert) CNMG120408-CQ
4 Tool holder MCNLNR2525M12
5 Tool geometry −6°, −6°, 6°, 6°, 95°, 95°, 0.8 mm
6 Cutting speed (m·min−1) (V c) 75, 110, 145
7 DOC (mm) (d c) 0.4, 0.8, 1.2
8 Feed rate (mm·rev−1) (f) 0.11, 0.178, 0.246
9 Air compressor Single phase
10 Pressure 6bar
11 Lubricant used Olive oil
12 Nanoparticles MoS2
13 SR tester Handysurf E-35B
14 Thermocouple Infrared with range −50 to 580°C
15 Cooling system used Dry, MQL, NMQL

2.7 Design of experiment

A statistical method called RSM has been used to examine the number of variables that together affect the output (response). The technique aims to establish the optimal set of variables to get the most effective value [49,50]. Furthermore, a mathematical model to forecast the reaction within specific ranges of process parameters is useful. The regression equation represents a surface when any two process factors are shown against the response, which makes it simpler to see how different process variables affect the response. The different conditions are shown in Table 4. There are in total 30 experiments consisting of 3 levels of cutting speed and 3 levels of feed rate followed by an equal number of DOCs. In addition to this, three different types of cooling systems such as dry, MQL, and NMQL have been utilized for finding its impact on the output parameters like SR and temperature. Post experimentation, ANOVA analysis has been conducted to find the significant parameters and its contribution to the output response. Moreover, the regression equation has been written as per the output of the ANOVA analysis. Finally, the confirmation experiments have been conducted according to points described by the ANOVA examination followed by a comparison between the predicted and observed values. The RSM technique was applied to find suitable design parameters, it is important in modeling and analysis of a wide range of factors affecting the outcome. In present experimentation, central composite design was used for the execution of the tests. The primary composite design was subjected to 30 tests and central composite design is split into three parts. (i) Numeric factor or independent factor, (ii) low and high values, and (iii) response. Four independent input parameters speed, feed, DOC, and environmental condition, and two output parameters surface integrity and temperature were selected. Three environments dry, MQL and NMQL represented (−1,0,1).

Table 4

Details of input parameters and output responses

Std Run Cutting speed (m·min−1) Feed rate (mm·rev−1) DOC (mm) Environment SR (Ra) Temperature (°C)
13 1 75 0.11 1.2 1 (NMQL) 1.67 134.2
3 2 75 0.246 0.4 −1 (Dry) 2.48 130
7 3 75 0.246 1.2 −1 (Dry) 2.68 143
30 4 110 0.178 0.8 0 (MQL) 1.53 150
10 5 145 0.11 0.4 1 (NMQL) 1.69 98
26 6 110 0.178 0.8 0 (MQL) 1.48 160
6 7 145 0.11 1.2 −1 (Dry) 2.44 170
5 8 75 0.11 1.2 −1 (Dry) 2.37 139
4 9 145 0.246 0.4 −1 (Dry) 2.65 190
16 10 145 0.246 1.2 1 (NMQL) 1.38 144
19 11 110 0.11 0.8 0 (MQL) 1.42 132.9
8 12 145 0.246 1.2 −1 (Dry) 2.53 196
11 13 75 0.246 0.4 1 (NMQL) 1.53 110
21 14 110 0.178 0.4 0 (MQL) 1.72 170
22 15 110 0.178 1.2 0 (MQL) 1.83 185
28 16 110 0.178 0.8 0 (MQL) 1.62 147
29 17 110 0.178 0.8 0 (MQL) 1.69 142
12 18 145 0.246 0.4 1 (NMQL) 1.47 115
24 19 110 0.178 0.8 1 (NMQL) 1.56 120
18 20 145 0.178 0.8 0 (MQL) 1.58 135
17 21 75 0.178 0.8 0 (MQL) 1.59 134.8
9 22 75 0.11 0.4 1 (NMQL) 1.48 90
20 23 110 0.246 0.8 0 (MQL) 1.63 155
15 24 75 0.246 1.2 1 (NMQL) 1.63 110
25 25 110 0.178 0.8 0 (MQL) 1.59 164
14 26 145 0.11 1.2 1 (NMQL) 1.46 110
1 27 75 0.11 0.4 −1 (Dry) 2.25 115
23 28 110 0.178 0.8 −1 (Dry) 2.51 178
2 29 145 0.11 0.4 −1 (Dry) 2.58 146
27 30 110 0.178 0.8 0 (MQL) 1.59 165

3 Results and discussion

In the machining process, heat creation at the tooltip is frequently seen between the tool and the workpiece due to rubbing or shear [51,52,53]. The creation of heat is caused by friction, which may cause destructive effects on the metal surface, tool life, and environment [54,55]. A major portion of heat, i.e., nearly 80% is generated on the chip and only 20% on the tooltip. This is known as the secondary deformation zone [56]. The output results obtained for SR and temperature have been listed in Table 4.

As per values of output responses, ANOVA analysis has been performed, where the terms like p values of a particular variable less than 0.05 show its significance, otherwise, the model is non-significant. Further, the lack of fit should be non-significant, if the value lies more than 0.44 indicating the signal strength of the fit. On the other side, if the value obtained is less than 4, then the fit is significant and not suitable for the model. Moreover, the entities like adjusted value and predicted value are close to the R 2 value. If the difference is more than 2, then the model is not suitable for the experiment. Adequate (Adeq.) precision also known as S/R ratio has a large range of signal received up to 33%.

Based upon experimental observation, the predicted results vs actual result for SR and temperature has been drawn as shown in Figures 3 and 4. It is found that the actual value is nearest to the predicted value so the model is significant.

Figure 3 Predicted vs actual value for SR.
Figure 3

Predicted vs actual value for SR.

Figure 4 Predicted vs actual value for temperature.
Figure 4

Predicted vs actual value for temperature.

Further, Figures 4 and 5 show the nominal plot for roughness and temperature. The graph depicted that the actual value of SR and temperature lies nearest to the predicted value signifies the importance of the developed model and observations. Also, the values are normally distributed along the line and spread in the region of −3 to 3 (Figure 6).

Figure 5 Normal probability plot for SR.
Figure 5

Normal probability plot for SR.

Figure 6 Nominal plot for temperature.
Figure 6

Nominal plot for temperature.

As described in Table 6, the model is significant having p-value less than 0.05, along with this other input parameters like A, B, C, D, AB, AD, A 2, B 2, C 2 are valuable. Apart from this, the statistical entities such as R², Adjusted R², Predicted R², Adeq. Precision were found effective to indicate the validity of an observation.

In addition to this, the regression equation obtained for SR and temperature has been listed in equations (1) and (2). The final actual factor equation for SR is given below:

(1) SR = 1 .60 − 0.0072 × A + 0.0439 × B + 0.0206 × C − 0.4783 × D − 0.0362 × A B − 0.0675 × A C − 0.0350 × A D + 0.0025 × B D − 0.0162 × C D − 0.0350 × A 2 − 0.1245 × B 2 + 0.15555 × C 2 + 0.4155 × D 2 .

The actual factor equation for temperature is given below:

(2) Temp = 155 .34 + 12.17 × A + 7.94 × B + 9.46 × C − 22.04 × D + 7.20 × A B − 1.57 × A C − 9.32 × A D − 2.20 × B C − 3.20 × B D + 2.08 × C D − 21.11 × A 2 − 12.06 × B 2 + 18.49 × C 2 − 7.01 × D 2 ,

where the terms A, B, C, and D represent the cutting speed, DOC, feed rate, and different environments mentioned in Tables 4–6. The highest temperature was observed in the shear plane during dry conditions which is not suitable for both the surface texture and tool life [57,58,59]. SR decreases with the increase in the cutting velocity and feed rate but decreases with the increase in the DOC in some conditions. High plastic deformation noted during increasing DOC result as a least surface finish and high temperature [60]. MQL and NMQL provide better surface finish and helps to improve tool life also. MQL and NMQL reduce temperature by up to 25 and 40% than dry machining [61]. It improves tool life and decrease the manufacturing costs than the dry condition. Surface texture and temperature increase with feed rate but decrease at high DOC and velocity [62].

Table 5

ANOVA analysis of SR

ANOVA for SR
Source Sum of squares df Mean square F-value p-value Remarks
Model 5.67 14 0.4053 122.21 <0.0001 Significant
A – cutting Speed 0.0009 1 0.0009 0.2831 0.6025
B – feed Rate 0.0347 1 0.0347 10.46 0.0056
C – DOC 0.0076 1 0.0076 2.29 0.1507
D – environment 4.12 1 4.12 1241.88 <0.0001
AB 0.021 1 0.021 6.34 0.0237
AC 0.0729 1 0.0729 21.98 0.0003
AD 0.0196 1 0.0196 5.91 0.0281
BC 0.0001 1 0.0001 0.0302 0.8645
BD 0.0841 1 0.0841 25.36 0.0001
CD 0.0042 1 0.0042 1.27 0.2767
A² 0.0031 1 0.0031 0.9285 0.3505
B² 0.0401 1 0.0401 12.1 0.0034
C² 0.0627 1 0.0627 18.9 0.0006
D² 0.4474 1 0.4474 134.89 <0.0001
Residual 0.0497 15 0.0033
Lack of fit 0.0234 10 0.0023 0.4445 0.8711 Not significant
Pure error 0.0263 5 0.0053 — — —
Cor total 5.72 29 — — —
Fit statistics
Std. Dev. 0.0576 R² Adjusted R² Predicted R² Adeq. precision
Mean 1.85 0.9913 0.9832 0.9676 33.3029
CV (%) 3.12
Table 6

ANOVA analysis of cutting temperature

Response: ANOVA analysis of temperature
Source Sum of squares Df Mean square F-value p-value Remarks
Model 21133.74 14 1509.55 17.25 <0.0001 Significant
A – cutting speed 2664.5 1 2664.5 30.45 <0.0001
B – feed rate 1134.47 1 1134.47 12.97 0.0026
C – DOC 1609.34 1 1609.34 18.39 0.0006
D – Environment 8747.24 1 8747.24 99.97 <0.0001
AB 829.44 1 829.44 9.48 0.0076
AC 39.69 1 39.69 0.4536 0.5109
AD 1391.29 1 1391.29 15.9 0.0012
BC 77.44 1 77.44 0.885 0.3617
BD 163.84 1 163.84 1.87 0.1913
CD 68.89 1 68.89 0.7873 0.3889
A² 1154.27 1 1154.27 13.19 0.0025
B² 376.64 1 376.64 4.3 0.0556
C² 886.07 1 886.07 10.13 0.0062
D² 127.21 1 127.21 1.45 0.2466
Residual 1312.48 15 87.5
Lack of fit 849.15 10 84.91 0.9163 0.5781 Not significant
Pure error 463.33 5 92.67
Cor total 22446.22 29
Fit statistics
Std. Dev. 9.35 R² Adjusted R² Predicted R² Adeq. precision
Mean 142.33 0.9415 0.887 0.7495 16.4458
CV (%) 6.57

SR increases with the improvement in the feed rate and decreases with the increase in the speed. Figure 7 shows 3D relation between speed, feed rate, and SR. In this graph, it has been observed that SR increases with the increase in speed and feed rate and DOC as visible in Figure 7. Higher plastic deformation occurs between the tool tip and workpiece at the large DOC as well as on the feed rate which ultimately effect on SR. [63]. Built up edges formed in higher feed rate and DOC that increase SR. At higher cutting speed, more friction will occur which is also responsible for the increase in the SR.

Figure 7 Impact of cutting speed vs feed rate on SR.
Figure 7

Impact of cutting speed vs feed rate on SR.

SR decreases with the increased cutting velocity but increases in higher DOC as shown in Figure 8. Due to greater temperature and shear force generated between tool tip and workpiece which further resulted in restricting the formation of continuous chips. This shear force breaks down the chip into small chips which helps to improve roughness. The lower surface finish is produced due to higher feed range and large load on tool tip because of higher DOC [64,65].

Figure 8 Impact of cutting speed and DOC on SR.
Figure 8

Impact of cutting speed and DOC on SR.

At highest DOC and feed rate, dull surface is obtained during machining as shown in Figure 8. A suitable machining condition has been obtained at lower feed rate and DOC. It affects slightly on the surface integrity. The least DOC with speed gives better SR shown in Figure 8. Greater shear force observed at higher DOC and feed rate is responsible for creating high temperature and BUE formed in the chip. The high temperature may produce worm up edges which is harmful for the surface, lower DOC and speed can be preferred for machining of magnesium alloy because less temperature and less force is produced and does not produce any effect on surface.

Cooling environment directly affects the SR as visible in Figure 10. During dry conditions, higher roughness was obtained, but during MQL and NMQL lower roughness was obtained due to better cooling and favorable lubrication action. In dry condition (−1), less surface finish was recorded in high velocity but while using MQL and NMQL, it improved up to 25 and 40% as shown in Figures 9 and 10. Dull surface is obtained at highest DOC with lower cutting velocity due to high cutting temperature generated between cutting tool and it improves with lower DOC and highest cutting speed. Least surface finish was obtained during dry condition (−1) with high velocity. MQL (0) and NMQL (1) produces better surface finish. Rise in DOC produces more waviness surface due to larger forces on cutting tool and the effect is illustrated in Figure 10.

Figure 9 Impact of DOC and feed rate on SR.
Figure 9

Impact of DOC and feed rate on SR.

Figure 10 Impact of cooling environments and cutting speed on roughness.
Figure 10

Impact of cooling environments and cutting speed on roughness.

It is observed that cooling environment and DOC play major role on the SR. In dry condition, the highest roughness has been achieved. With the improvement in DOC the temperature will increase and directly affect the SR. MQL and NMQL provide better cutting environment than dry. Both MQL and NMQL is suitable for high speed machining for magnesium alloy. It helps to improve productivity. An increase in cutting speed will result in higher temperature along with an elevation in feed rate due to larger friction and chip load on the cutting tool as explored in Figure 11.

Figure 11 Impact of cooling environment condition and DOC on SR.
Figure 11

Impact of cooling environment condition and DOC on SR.

Temperature is a major concern in achieving the surface finish of the product. A lot of heat is generated in the shear plane due to friction which causes plastic deformation during the machining process, responsible for poor surface finish and least tool life. Magnesium AZ91D is also difficult to cut due to its lower melting point. When its temperature reaches more than 500°C, then it may cause blasting and reduce the tool life. It is observed that during turning at a lower feed rate, the contact between tool and material surface is increased, thus, it can increase the temperature between tool and material surface as a consequence of high temperature and least surface finish. On the other hand, higher cutting velocity and least DOC recorded the lowest temperature and high surface finish because contact time and friction between tool and job have been reduced as shown in Figure 12. Temperature increased with DOC and cutting velocity. The highest temperature range was obtained during dry condition at a high-velocity rate.

Figure 12 Impact of feed rate and cutting speed on temperature.
Figure 12

Impact of feed rate and cutting speed on temperature.

It has been seen that MQL and NMQL reduced the temperature frequently and helped to improve SR and tool life as shown in Figure 13. Larger temperature has been obtained at a higher cutting speed and DOC. It directly affects surface integrity and tool life. It has been found from Figures 12–14 that temperature increases with the increment in velocity, whereas temperature rises with feed rate and DOC. On the other side, temperature variation has been found in different environments like dry, MQL, and NMQL. From Figure 13, it has been noticed that least temperature has been recorded during cooling condition (1), i.e., NMQL system. It may be due to heat absorption effect of nanoparticles along with cooling action of MQL jet mixed with air at pressure 5 bar. Moreover, addition of nanoparticles to vegetable oil provides lubrication action plus cooling action of MQL jet air assisted with nanoparticle cooling action. All these combined effects resulted in lower cutting temperature and SR in NMQL environment.

Figure 13 Impact of cutting speed and DOC on temperature.
Figure 13

Impact of cutting speed and DOC on temperature.

Figure 14 Impact of cooling environment and cutting speed on temperature.
Figure 14

Impact of cooling environment and cutting speed on temperature.

Lowest cutting temperature can be obtained up to some extent in dry conditions, and increase with the increase in the cutting speed which is not suitable machining. MQL and NMQL are able to machine at high speed without increase in temperature. Figure 14 shows that in dry environment, high friction occurring between tool and work piece at large speed is responsible for generating higher temperature on tool tip. This temperature is responsible for creating worm edge during machining of magnesium alloy AZ91D. These edges may damage the surface of the work piece and produce dull roughness.

A high feed rate results in a higher temperature obtained in dry condition but NMQL gives greater results than dry and MQL conditions shown in Figure 15. Higher DOC results in greater temperature, MQL and NMQL show better results but NMQL is better than both dry and MQL conditions. It has been seen that a large amount of temperature is reduced in NMQL condition because the nanoparticles tend to improve thermal conductivity as shown in Figure 4(f).

Figure 15 Impact of DOC and feed rate on temperature.
Figure 15

Impact of DOC and feed rate on temperature.

The highest temperature was obtained with higher DOC in dry conditions than the MQL and NMQL. The temperature improved with the increase in DOC continuously. Feed rate is another factor to be considered. Temperature improved with the increase in feed rate. At lowest feed rate, temperature is less for dry condition, if feed increased, then temperature will increase. Less temperature improvement is recorded in MQL and NMQL than dry condition. As described in Figures 14 and 15, feed rate, cooling environment, and DOC have influence on the temperature significantly (Figure 16).

Figure 16 Impact of cooling environment and DOC on temperature.
Figure 16

Impact of cooling environment and DOC on temperature.

To check the accuracy of the developed model confirmation, experiments were conducted at different runs like 5, 20, and 29. After the confirmation experiments, observed values and predicted values are listed in Table 7. The percentage of errors was calculated among observed and predicted values. It has been observed that the maximum percentage of error for SR was calculated as 1.89 at run 20. Similarly, the larger percentage of error for temperature was observed at –5.0 during run 20. Further, it has been depicted that the percentage of error among predicted and observed values was less than 5% which indicates the accuracy of the developed model at a 95% confidence interval.

Table 7

Confirmation of developed model

Runs 5 20 29
Cutting speed (RPM) 145 145 145
Feed rate (mm·rev−1) 0.11 178 0.11
DOC (mm) 0.40 0.8 0.4
Cooling environment NMQL (1) MQL (0) Dry (−1)
Actual values of SR 1.65 1.58 2.45
Predicted values of SR 1.62 1.559 2.47
% error SR 1.81 1.89 −0.81
Actual values of temp. 94 139 152
Predicted values of temp. 90.36 146 150.85
% error temperature 3.8 −5.0 0.75

4 Conclusion

From experimental observation and the developed model, the following conclusions were drawn:

  1. Roughness increases with the increase in speed, feed rate, and DOC. In dry condition, lower velocity and DOC provided better SR.

  2. NMQL and MQL system has reduced the temperature and SR during high cutting speed. NMQL performed better than other conditions.

  3. It was observed that cutting speed plays a major impact on surface finish than DOC and feed rate in dry conditions, moderate speed is favorable for achieving a higher surface finish.

  4. DOC and feed rate have a minor influence on the SR during the turning of AZ91 Mg Alloy.

  5. Higher temperature causes plastic deformation between the tool tip and workpiece AZ91D at a large cutting velocity. It may result in a poor surface finish and forming of burnt-up edge. MQL and NMQL help to avoid such kind of act during machining and higher cutting velocity can be achieved.

  6. MQL and NMQL have reduced the cutting temperature by 25% and 40% than the dry conditions which helps to increase tool life and SR.

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU) for funding this research through the Research Group Program Under the Grant Number: (R.G.P.2/592/44).

  1. Funding information: The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU) for funding this research through the Research Group Program Under the Grant Number: (R.G.P.2/592/44).

  2. Author contributions: Conceptualization: A.K., S.S.G., G.S., S.S., S.P.D., and K.A.M.; methodology: A.K., S.S.G., G.S., S.S., S.P.D., and K.A.M.; formal analysis: A.K., S.S.G., G.S., S.S., S.P.D., and K.A.M.; investigation: A.K., S.S.G., G.S., S.S., S.P.D., and K.A.M.; writing – original draft preparation: A.K., S.S.G., G.S., S.S., S.P.D., and K.A.M.; writing – review and editing: S.S., K.S., D.K., A.H., and M.A.; supervision: S.S., K.S., D.K., A.H., and M.A.; project administration: S.S., K.S., D.K., A.H., and M.A.; funding acquisition: S.S., K.S., D.K., A.H., and M.A. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Ethical approval: Not applicable.

  5. Consent to participate: Not applicable.

  6. Consent to publish: All authors have read and approved this manuscript.

  7. Data availability statement: The manuscript has no associated data.

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Received: 2023-08-26
Revised: 2024-02-07
Accepted: 2024-02-20
Published Online: 2024-06-24

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
https://doi.org/10.1515/htmp-2022-0328
Keywords for this article
dry machining; minimum quantity lubrication; nano-MQL; RSM; ANOVA; turning
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