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Exploring the Impact of Turning of AISI 4340 Steel on Tool Wear, Surface Roughness, Sound Intensity, and Power Consumption under Dry, MQL, and Nano-MQL Conditions

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22 September 2023

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27 September 2023

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Abstract
Optimizing input parameters not only improves production efficiency and processing quality but also plays a crucial role in the development of green manufacturing engineering practices. The aim of the present study is to conduct a comparative evaluation at cutting performance and machinability process during the turning of AISI 4340 steel under different cooling conditions. The study analyses cutting operations during turning using dry, minimum quantity lubrication (MQL), and nano-MQL. As control parameters in the experiments, three different cooling types, cutting speed (100, 150, 200 m/min), and feed rate (0.1, 0.15, 0.20 mm/rev) levels were applied. The experiment results present that the optimal output values are found as Vb=0.15 mm, Ra=0.81µm, 88.1 dB for sound intensity, and I=4.18 A for current. Moreover, variance analysis (ANOVA) was performed to determine the effects of input parameters on response values. Under dry, MQL, and nano-MQL processing conditions, parameters affecting tool wear, surface roughness, current by the motor shaft, and sound level were examined in detail, along with the chip morphology. The responses obtained were optimized according to the Taguchi S/N method. As a result of optimization, it was concluded that the optimum values for cutting conditions (A3B1C1) were nano-MQL cooling and V=100 m/min, f=0.1 mm/rev cutting. Last but not least, it was observed that there was a 13% improvement in tool wear, 7% in current, 9% in surface roughness, and 8% in sound intensity compared to the standard conditions. In conclusion, it was determined that nano-MQL and the lowest level of cutting and feed rate values provided the optimum results.
Keywords: 
Subject: Engineering  -   Industrial and Manufacturing Engineering

1. Introduction

AISI 4340 steel, which contains elements such as nickel, molybdenum, and chromium in different proportions, is a commercial steel known for its good corrosion resistance, hardenability, ductility, and high strength. It finds applications in various fields such as the aerospace industry, automotive industry, power plants, maritime and defense industries, petroleum and gas industry, and weapons industry, especially after undergoing different heat treatments [1]. Considering the areas of use of this material, it is classified as a challenging alloy due to its high strength and low thermal conductivity. Consequently, excessive heating occurs in the machining zone, leading to issues such as poor surface finish and excessive tool wear. Cooling of the machining zone is of paramount importance to address the problems caused by excessive heat. Many studies have been conducted to optimize cutting parameters of AISI 4340 alloy steel and improve the quality of the machined surface[2-5]. In these studies, researchers have focused on reducing surface roughness to ensure the integrity of the machined surface, minimizing tool wear and the resulting increase in cutting forces, reducing power consumption, enhancing machining efficiency, optimizing cutting parameters such as cutting speed and feed rate, as well as controlling parameters like cooling and lubrication methods, and exploring environmentally friendly green manufacturing[6].
The limited availability of resources worldwide necessitates optimization in production and environmental aspects. However, the increasing demand for prosperity in developing societies leads to increased resource consumption. As resource consumption rises, it becomes imperative to minimize environmental pollution and harmful emissions. Similarly, in the field of metalworking, particularly in chip manufacturing, the growing importance of human health and environmental awareness necessitates new studies on the use of cutting fluids[7]. Compared to traditional methods, the cost of lubricating/cooling cutting fluids is estimated to be around 17% of the total production costs within machining. Besides the high cost, the environmental and health hazards associated with these fluids have prompted researchers to explore new cooling techniques[8,9]. Removing heat generated during the machining of some high-hardness materials is a significant challenge. Although conventional coolants seem to address the issue of high heat generation, the high temperatures can lead to thermal softening in both the workpiece and the tool. Cutting fluids directed to the machining zone can adversely affect the metallurgical properties of cutting tools and shorten their lifespan[10]. Therefore, dry machining has continued to be used for various types of materials in chip manufacturing as it eliminates such drawbacks associated with traditional fluids[11,12].
Minimum Quantity Lubrication (MQL) refers to the mixture of pressurized air in the form of fine droplets that atomize and create a spray of a small quantity of lubricating liquid in the cutting zone. The intensity and quantity of the aerosol cloud can be controlled using various valves[13]. Traditionally, abundant cutting fluid is used when machining certain materials. However, this approach increases component costs and energy consumption. Therefore, researchers have found it a compelling reason to explore alternative lubrication-cooling strategies and enhance cutting performance. In a study by Gong et al.,[14] machining of Inconel 718 alloy was carried out under Cryo, dry, wet, MQL, and nanofluid MQL conditions. The results indicated that Cryo conditions provided good surface quality even at high cutting speeds and reduced chip compression ratios.
The application of MQL in machining operations has demonstrated various benefits, including limited environmental impact compared to the abundant use of traditional cutting fluids, reduced production costs, and increased worker safety. However, MQL has its limitations in terms of cooling function due to the inability to completely limit heat generation in both primary and secondary machining zones, mainly because of the lower oil flow rate. Hence, there is a need to enhance cutting performance in MQL processes, and hybrid nanofluid-assisted MQL applications have recently become important research trends to improve MQL efficiency[15,16]. For a lubrication system using MQL and nano-MQL to be effective on cutting, the quantity of liquid delivered to the work zone per minute and the air pressure, as well as the type and quantity of nano material added to the cutting fluid, are effective parameters. Patole and Kulkarni optimized cutting parameters by adding 0.02% MWCNT to the cutting fluid during turning of AISI 4340 steel. Four pressure values, four flow rate values, and two types of nano fluids were used as cutting parameters. The results indicated that the nano-fluid coolant was highly effective on the parameters, with flow rate and pressure following suit. The optimal values for these parameters were determined to be 5 bars of pressure and a flow rate of 140 mL/min. They claimed that using MWCNT-doped nano-MQL systems could reduce tool wear and achieve good surface roughness[17]. Palanisamy et al. investigated the effect of applying high-pressure cutting fluid to the cutting zone during the machining of titanium alloys. They achieved better surface quality on the machined material and a longer tool life. The study also revealed that cooling fluids at different pressures caused significant changes in chip morphology[18].
In a study by Ramanan et al., nano fluids with higher thermal conductivity compared to traditional cutting fluids were used in the MQL system to investigate their effect on cutting parameters. The experiments found that wear values obtained with normal cutting fluids were much higher than those with MQL and nano-MQL. Other outcomes of the study included specific cutting energy, surface roughness (Ra, Rz), and crater wear on the cutting tool[19].
Studies on cooling fluids with minimal environmental impact and workplace safety concerns continue from the perspective of operators[20]. In this regard, Çetan et al. compared Al2O3 nano particle-based nMQL with cryogenic cooling during the turning of Nimonic 90 alloy. The response outputs of the study were determined as surface quality, tool wear, and cutting forces. They noted that the cryogenic cooling method provided better results in machining the alloy[21].
The most desirable characteristics of cutting fluids include efficient cooling (with superior convective and conductive heat transfer coefficients), effective lubrication, and efficient removal of chips from the machining zone. Effective heat dissipation from the cutting zone significantly reduces tool wear. Reduced wear, in turn, positively contributes to lower forces on the machine tool, reducing power consumption and improving surface roughness.
Modern machining industries primarily prioritize factors such as workpiece dimensional accuracy, surface finish, cutting temperature, high production rates, extended cutting tool lifespan, cost savings, occupational health, machining performance, and energy consumption reduction, especially with a focus on environmental concerns. Surface roughness and noise pollution are crucial factors, and various studies in the literature have demonstrated that machining under Minimum Quantity Lubrication (MQL) and nano-MQL conditions leads to improvements in energy consumption, surface roughness, and cutting forces[22,23].
Sahinoglu and Rafighi conducted turning experiments on AISI 4140 alloy steel under dry conditions. They utilized control parameters such as feed rate, cutting speed, and depth of cut, while measuring outputs including surface roughness, vibration, sound intensity, and machine current. Statistical analysis was applied to assess the effects of input variables on response variables using the response surface method, analysis of variance, and regression equations. According to their findings, both depth of cut and feed rate significantly impact sound intensity[24].
Recently, numerous researchers have investigated the balance between cutting quality and power consumption during machining operations. The continuous rise in energy demand and the constraints associated with increasing carbon emissions have exerted significant pressure on manufacturing industries to save energy. In one study, Abbas et al. explored the surface roughness and power consumption of AISI 1045 steel when machined in a nanofluidized MQL environment. They reported achieving sustainable performance and minimal power consumption as a result of applying a minimal amount of nanofluid-based lubrication[25]. In a review study, Sharma et al. compared the cutting performance of different types of nano-MQL in various machining processes. Their findings suggested that increasing nanoparticle concentration leads to improved lubrication, reduced surface roughness, lower cutting forces, extended tool life, and reduced power consumption[26].
Sound levels during machining processes are parameters of particular concern for occupational health and safety. In the machinery manufacturing industry, especially in large facilities with numerous machine tools in close proximity, machine operators are exposed to high levels of noise. It is essential to mitigate noise levels in this context. Svenningsson and Tatar investigated sound generation using different cutting methods and inserts in a study on AISI 4340 material. Through their analysis and measurements, they proposed that the source of the sound is related to the vibration mode of the chip. Chip segmentation influences cutting forces, thereby increasing the current[27].
Albayrak et al. investigated the effects of cutting parameters, including feed rate, spindle speed, and chip depth, at three levels, on the sound level and surface roughness during the turning of SAE 4140 alloy steel. According to the experimental results, they indicated that spindle speed was the most influential parameter on sound levels, while feed rate had the most significant impact on surface roughness. They identified the optimal cutting parameters as follows: a feed rate of 0.1 mm/rev, a spindle speed of 750 rev/min, and a chip depth of 0.5 mm for sound levels and surface roughness[28]
In a study by Downey et al., the machining precision, tool wear performance, and surface roughness of AISI 4340 material were experimentally compared using the Acoustic Signals (AS) method with a High-Speed Steel (HSS) cutting tool and a Physical Vapor Deposition (PVD) Titanium Carbon Nitride (TiCN) insert. The samples were machined at parameters of 130 rev/min cutting speed, 120 m/min feed rate, and 0.20 mm cutting depth, using a cooling fluid, and they were machined at various durations. The study divided the machining time into three phases, analyzing the wear values and corresponding sounds for each phase, which were transferred to a computer environment through a microphone. It was reported that as tool wear progressed over time, the emitted sound changed, and a unique sound signature characterizing wear was observed for each wear phase[29].
Through a review of the literature, it becomes evident that there is a significant lack of studies covering various aspects, including the cooling conditions of untreated.
AISI 4340 alloy steel, environmental consciousness in line with green manufacturing, the impact of sound during machining on both human health and cutting parameters, wear, surface roughness, and energy consumption. This study aims to contribute to previous research by statistically evaluating the reciprocal effects of dry, MQL, and nano-MQL cooling under different machining conditions, focusing on chip morphology. The study employs the Taguchi Signal-to-Noise (S/N) method to optimize tool wear, power consumption, sound levels reduction, and surface roughness.

2. Materials and Methods

2.1 CNC Lathe, Workpiece, and Cutting Tool

For the turning experiments of AISI 4340 steel, workability criteria such as surface roughness, tool wear, sound intensity, and the current during machining were measured. The experiments were conducted using a Yunnan Cy-K360n CNC Lathe with a maximum of 10,000 rpm and a 7.5 kW main motor power. Commercial AISI 4340 (DIN 34CrNiMo6) steel, in its as-received condition without any physical or chemical treatments (e.g., heat treatment, etc.), was used as the workpiece material. The round bar-shaped AISI 4340 (DIN 34CrNiMo6) steel was cut using a band saw with a cooling fluid to obtain dimensions of 150 mm in length and Ø260 mm in diameter. The chemical properties of the AISI 4340 alloy steel used in the experiments are provided in Table 1, while the mechanical properties are given in Table 2. The cut workpiece was prepared for the experiments by performing surface and face turning to remove surface impurities and oxide layers, ensuring that the workpiece was clean before commencing the experiments. All experiments involved the machining of a 135 mm-long surface. The CNC lathe machine and the workpiece used in the experiments are depicted in Figure 1.
In the experiments, PVD-coated cutting inserts of TaeguTec brand, DNMG 150608 TT5080 type, were used. For each experiment, one face of the cutting insert was utilized and recorded for wear measurement. The cutting insert has approximate dimensions of 15 mm in width, 6 mm in thickness, and a corner radius of 0.8 mm. The dimensions and appearance of the cutting insert are provided in Figure 2.

2.2. MQL System and Preparation of Nano Fluid

The primary objective of the MQL (Minimum Quantity Lubrication) system is to achieve optimal chip removal from the workpiece under environmentally friendly conditions, taking into consideration factors such as cost, health, and the environment. In this process, water-based biodegradable lubricants, which are environmentally friendly, are atomized and sprayed into the cutting area under high pressure, reducing the heat generated between the chip and the tool and carrying it away from the cutting zone. In the experiments, the S.B.H. STN 15 MQL system, which operates within a range of 4-6 bar air pressure as shown in the schematic representation in Figure 3, was used. The MQL flow rate varies in the literature between 60-300 mL/h. Several studies have reported improved surface roughness at higher flow rates[30]. In accordance with the literature, a flow rate of 100 mL/h was used in the experimental study.

2.2. Preparation of MQL System and Nano Fluid

Nano-fluid is a new class of fluids obtained by mixing nano-sized materials into the coolant fluid. In this study, Multi-Walled Carbon Nanotubes (MWCNT) with a diameter of 7 nm and a length of 5 μm were mixed with the cutting fluid. Triethanolamine was used as the cutting fluid. Triethanolamine is an environmentally friendly choice as it is a water-based chemical compound. Mixing was carried out by adding 1% by weight of MWCNT to 1 liter of cutting fluid. The mixing process of the nanomaterial into the cutting fluid occurred in three stages (Figure 4). In the first stage, the nanomaterial was weighed and then introduced into the cooling fluid, followed by mechanical stirring at 750 rpm for 1 hour. This step was aimed at preventing the clumping of the nanomaterial within the liquid. In the second stage, ultrasonic mixing was performed for 1 hour, and in the third stage, magnetic stirring was carried out at 1500 rpm for 2 hours to prevent the settling of the nanomaterial. The mixed nanomaterial was directly used in the experiments to prevent any settling.

2.3. Measurement of Experimental Outputs

Surface roughness is one of the essential factors in machining operations on metals. The quality of the machined surface is significantly represented by surface roughness. In this study, 'Ra' was considered as the roughness parameter. Surface roughness values were measured using the Dailyaid DR100 model roughness measurement device. For these values, the arithmetic average of measurements taken from three different points immediately after the machining experiment was calculated. The samples were thoroughly cleaned with air before measurements. The measurement device was axially placed on the workpiece. To reduce measurement errors, three different points were selected along the same axis for all experiments. Sampling length and evaluation length for surface roughness values were set at 0.8 mm and 4 mm, respectively.
Sound is a significant factor in terms of environmental pollution. Therefore, in the experiments, the sound generated by the machine and during cutting was selected as a parameter. To obtain reliable data during measurements, ambient sounds were isolated. Initially, the CNC lathe was run without any cutting to determine the background noise level, and values above this level were identified as cutting sound data. PCE brand and 322A model sound measurement device were used for measurements. The device has a measurement capacity within the frequency range of 30-130 dB and can capture data in the range of 1 second to 125 milliseconds.
The current value was measured using a UNI-T brand and UT202 model clamp ammeter. The device allows high-precision measurements with an extended current frequency. It has a 4000-count display and data-holding function, facilitating the analysis of measurement data. Due to its design, the clamp ammeter can only measure a single-phase current. Therefore, the phase going to the main spindle of the lathe was identified, and the clamp ammeter was mounted on this cable. During the experiment, the average of the varying current values was calculated.
The measurement of cutting tool wear amounts was performed using a Dino-Lite digital microscope. After a specific length of cutting operation, the cutting tool was removed, and photographs of the worn surfaces of the cutting tools were taken with the microscope to measure the side wear amounts.
Detailed images of the control parameters and outputs for the CNC used in the experiments are provided in Figure 4.

2.4. Cutting condition and design of experiment

During chip removal operations, a significant amount of electrical energy is primarily consumed, depending on various cutting parameters. In this study, the impact of input parameters was investigated with a focus on environmental consciousness and minimizing power consumption during machining. Particularly, as outputs of an environmentally friendly machining operation, surface roughness, tool wear, and, current, sound intensity a crucial factor for the environment were selected as output parameters in the optimization of control parameters. The control parameters selected included cutting speed and feed rate, taking into account the influence of environmental factors on methods for cooling the cutting zone. The levels of the fundamental cutting parameters were chosen in accordance with the recommendations of the tool manufacturer and existing literature. Preliminary experiments were conducted to test the interaction between the machine, cutting tool, and workpiece. The selected parameters and levels are shown in Table 3.
Experimental design is a method used to plan and conduct experiments. The Taguchi orthogonal array is a widely used statistical method for the analysis of process and product improvements. With this method, the best factors for obtaining the most optimal results with a small number of experiments can be determined. The Taguchi method has significant potential for the cost-effective analysis of experiments. Therefore, experiments were conducted using Taguchi's L9 orthogonal array, which uses three factors at three levels, in order to achieve the best results from a series of experiments. The impact levels of variables on the outputs were determined by applying Variance Analysis (ANOVA) to the experimental results with a 95% confidence interval. The experimental design and statistical analyses, according to the Taguchi method, were carried out using Minitab 20 software.
Table 4. Taguchi L9(33) ortogonal array.
Table 4. Taguchi L9(33) ortogonal array.
Exp. No MOC V (m/min) f (mm/rev) MOC V f
1 Dry 100 0,1 1 1 1
2 Dry 150 0,15 1 2 2
3 Dry 200 0,2 1 3 3
4 MQL 100 0,15 2 1 2
5 MQL 150 0,2 2 2 3
6 MQL 200 0,1 2 3 1
7 Nano-MQL 100 0,2 3 1 3
8 Nano-MQL 150 0,1 3 2 1
9 Nano-MQL 200 0,15 3 3 2
In this method, a statistical performance measure known as the Signal-to-Noise (S/N) ratio is used to analyze the results. The results obtained from the experiments are converted into Signal-to-Noise (S/N) ratios for evaluation. In the calculation of S/N ratios, three different methods, known as larger-the-better, smaller-the-better, and nominal-the-best, are used depending on the characteristic type. In determining the S/N values in this study, it was desired to minimize the SI values for noise pollution, maximize the surface roughness Ra for processing efficiency, minimize tool wear Vb, and minimize power consumption represented by I. Therefore, the formula corresponding to the "smaller-the-better" principle given in Equation 1 was used.
s m a l l e r   i s   b e t t e r ;         S N = − 10 log ⁡ 1 n ∑ i = 1 n y i 2
The objective here is to minimize the noise function, in other words, maximize the S/N ratio. Therefore, in the evaluations, the level with the highest S/N ratio among the calculated average S/N ratios for each parameter is used to determine the best result.

3. Result And Discussion

3.1. Effects of Process Parameters on Outputs

In this section, the effects of cutting speed and feed rate on the outputs in the turning of AISI 4340 steel under cooling conditions of dry, MQL, and MQL with MWCNT additive have been examined. For all experimental trials after turning operations, the measured tool wear, surface roughness, sound intensity, and the power consumption values calculated for the machining process, corresponding to the processed surfaces, are shown in Table 5. The interaction status of input parameters on the outputs obtained from the experiments has been compared with 3D graphics. In the study, experimental results were analyzed using ANOVA to determine the impact levels of control factors on the outputs. Response variables were transformed into signal-to-noise (SN) ratios in the Taguchi method. The calculated Taguchi Signal/Noise ratios are also given in the Table 5. As a result of the experiments, the values minimized as expected were observed in the experiments conducted with nano-MQL cooling, except for power consumption. All experiments, except for power consumption, minimized under condition A3B2C1, which corresponds to experiment number 8. These values occurred as Vb=0.15 mm, Ra=0.81 µm, 88.1 dB for sound intensity, and I=4.18 A for the current. This situation is also evident from the fact that the max. values of the S/N ratio are the same as the best values.

3.2. Analysis of Variance (ANOVA)

In statistical analysis, especially in the field of engineering, Analysis of Variance (ANOVA) is commonly used to evaluate experimental data. The aim of ANOVA is to determine to what extent the factors under investigation influence the selected output values that measure quality[31]. In this research, the direct interaction of control factors (MOC, V, f) on the outputs was analyzed using ANOVA. In the ANOVA table, when the p-value is less than 0.05, the regression equation and the examined factors are considered statistically significant. In addition, the Percentage Contribution Ratio (PCR) of the terms in the estimated model to the total variation can be checked to assess the degree of influence of the factors on the model.
The results of the variance analysis calculated for all outputs are given in Table 6. The table shows the F values and the Percentage Contribution Ratio (PCR) indicating the significance level of each variable. This analysis was performed with 95% confidence interval and 5% significance levels. The effect of control factors is determined by comparing the F values. The factor with the highest F value has the greatest impact on the result. The significance of the results is determined by the p-value. A p-value less than 0.05 indicates that the factor is statistically significant. It can be seen from the table that for tool wear, MOC and f values are both less than 0.05. According to the table, the factor with the highest contribution ratio for tool wear is MOC with 57.06%, followed by feed rate with 34.06%, and cutting speed with the least contribution of 4.84%.
According to the table, for current, MOC is the most effective parameter with 59.94%, followed by feed rate with 29.6%, and cutting speed with the least contribution of 2.46%. The p-values indicating statistical significance are 0.002≤0.05 for MOC and 0.008≤0.05 for feed rate, making them statistically significant. Cutting speed, on the other hand, is greater than 0.05, indicating insignificance.
When examining the ANOVA table for surface roughness, the p-values calculated for MOC and feed rate are less than 0.05, indicating that these factors are statistically and physically significant in terms of surface roughness. The p-value for cutting speed is greater than 0.05, indicating that this parameter has no significant effect on surface roughness. According to the table, the factor with the highest contribution ratio for surface roughness is feed rate with 54.89%, followed by MOC with 36.21%, and cutting speed with the least contribution of 1.40%.
In the table, according to the ANOVA results for sound intensity, it can be understood that MOC is the most effective cutting parameter on sound intensity with 67.27%, followed by feed rate with 27.30%, and cutting speed with a very low value of 1.30%, indicating very little effect. This situation is clearly seen in the F and P values. For MOC, P=0.002≤0.05 and f=0.007≤0.05 are significant, while V=0.151≥0.05 is insignificant.
The regression model is typically used to predict responses, representing the mathematical expression of the regression line obtained from the responses. It is used in conjunction with the error term to establish the relationship between responses and predictive parameters. First-degree regression equations modelling how the output parameters, namely tool wear, current, surface roughness, and sound intensity, change depending on input parameters have been provided in Equations 2, 3, 4, and 5, considering only main factor effects, based on the statistical and Taguchi analyses conducted.
V b = 0.1656 + 0.0690 M O C + 0.000393 V + 1.037 f
I = 4.815 − 0.863 M O C + 0.0035 V + 12.13 f
R a = 0.719 − 0.2883 M O C + 0.00113 V + 7.1 f
S I = 93.28 − 3.975 M O C + 0.01106 V + 50.64 f
R2 is a statistical measure that gauges the success of a regression analysis. The R2 value indicates how much of the variance in the dependent variable is explained by the independent variables. R2 takes a value between 0 and 1, where 0 means that the independent variables do not explain the dependent variable at all, and 1 means that the independent variables completely explain the dependent variable. In other words, a high R2 value indicates that the regression model fits the data well, while a low R2 value indicates a weak model. Adjusted R2 is similar to R2 but is used in regression models with multiple independent variables. Adjusted R2 also takes a value between 0 and 1.
The multiple regression coefficients of these first-degree equations have been modeled with high accuracy, at a 95% significance level, as follows: R2=92.10%, R2(adj)=87.35 for tool wear, R2=92.00%, R2(adj)=87.21 for current, R2=92.50%, R2(adj)=88.00 for surface roughness, and R2=95.87%, R2(adj)=93.40 for sound intensity. These values being greater than 85% indicate that the regression model fits well, and models close to reality can be obtained.

3.3. Tool Wear

Tool wear is the phenomenon where a tool experiences material loss and deformation in its structure due to the applied load on the cutting edge. Cutting parameters such as feed rate, cutting speed, and the cooling status of the cutting environment play a role in determining the level of tool wear, in addition to the characteristics of the workpiece and cutting tool. Flank wear is commonly used to determine the level of wear and is the most effective method used to predict tool life. The wear levels of the tools used in the experiments were measured, and their appearances were imaged under a microscope. When examining the images, it can be observed that non-uniform wear types such as chipping and breakage were not present in all tools, as shown in Figure 6. Burn marks were observed on the edges of the tools in dry cutting conditions due to the high heat generated. It is evident that this burn mark is significantly reduced in experiments conducted with MQL and nano-MQL. This can be attributed to the better cooling and lubrication properties provided by Nano cutting fluid. The limitation of wear only to flank wear on the tool edges can be attributed to the better conductivity, convection, and wetting properties of Nano cutting fluid[1].
The interaction of input parameters on tool wear obtained from experiments is presented in Figure 7. According to the graphs, when examining each variable, it can be seen that the feed rate has the highest effect on tool wear, especially under dry cutting conditions. Tool wear increases significantly due to the high heat generated between the tool and the workpiece in dry cutting conditions. In dry cutting conditions, as the feed rate increases, wear exhibits a linear increase, while this linearity is not observed with an increase in cutting speed. From the graphs, it can be observed that the lowest tool wear occurs at a moderate level of cutting speed, which is 150 m/min. In all parameters involving nano-MQL, less wear was observed at low speeds, but as the speed increased, more wear was observed[32]. This finding is consistent with other studies conducted on the subject[33].
The analysis of the effect of each control factor on tool wear was conducted using the signal-to-noise (S/N) ratio response table. The highest values in the table indicate the optimal levels. The Rank value for the MOC factor is 1, which provides the order of importance of variables. From this table, it can be concluded that the most important factor affecting the results is MOC. This result has been confirmed by the conducted variance analysis. Delta represents the difference between the maximum and minimum values of the respective variable. The largest difference in the Delta column indicates that the cooling method is the most important parameter among all controllable parameters. The second most important parameter is feed rate, and the least important parameter is cutting speed.
Table 7. S/N ratio response for tool wear (Vb).
Table 7. S/N ratio response for tool wear (Vb).
Level MOC V f
1 10.23 12.95* 14.2*
2 12.79 12.88 13.15
3 15.06* 12.25 10.73
Delta 4.84 0.71 3.47
Rank 1 3 2
In Figure 8, the effects of cutting parameter levels on tool wear were determined using the S/N ratio. The optimum levels of cutting parameters are seen to be A3B1C1 using the "smaller the better" ratio. The slope of the line clearly demonstrates the influence of each control factor. Considering the highest S/N ratio values, the optimum levels are as follows: Level 3 for MOC (nano-MQL), Level 1 for cutting speed (100 m/min), and Level 1 for feed rate (0.1 mm/rev).

3.4. Energy Consumption (Current)

As a result of the experiments, the lowest current value of 4.18 Amperes occurred in Experiment 8, under cooling conditions with nano-MQL, 150 m/min cutting speed, and 0.1 mm/rev feed rate. In the study, the current by the machine's spindle motor was measured. The energy consumption is obtained by multiplying the current, voltage, and cutting time. Therefore, to reduce the processing time, and consequently lower energy consumption, it is necessary to increase the cutting parameters of cutting and feed rate. Cutting speed and feed rate have a significant impact on power consumption because these parameters reduce processing time. Even though these parameters increase the current value somewhat, they significantly reduce the processing time. Therefore, to achieve minimum power consumption, it is necessary to increase the cutting parameters to reduce processing time[34]. When examining the graphs, it can be seen that the highest current value occurred at the highest values of cutting speed and feed rate under dry-cutting conditions.
In dry machining, in addition to its detrimental effect on tool wear, poor surface quality, increased sound intensity, and higher energy consumption in terms of amperage and power were observed in the graphs. MQL and nano-MQL cooling/lubrication applications, on the other hand, are more preferred for surface quality, cutting forces, and tool life[33]. In turning experiments, it can be seen that power consumption decreased with MQL and nano-MQL cooling/lubrication applications compared to dry machining conditions. The lubricant is provided as an aerosol with compressed air or as droplets at the work tool interface. The minimum amount of lubricant used allows the workpiece to be almost dry and chips to form[35]. Regardless of the cooling method, when looking at the graph showing the change in current value with cutting parameters in Figure 7, it can be seen that the steepest slope in the graph occurs at the highest values of cutting parameters. This indicates that increasing cutting speed and feed rate leads to the highest current value. Despite having the highest electricity consumption per unit time, an increase in these parameters actually means the least consumption since it shortens the total processing time for a certain volume of work.
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Figure 9. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Current.
Figure 9. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Current.
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The S/N (Signal-to-Noise) ratios for the changes in current concerning cutting parameters and their levels in the turning process are provided in Table 9. The optimization of measured values and determination of quality characteristics are achieved through S/N ratios. According to the table, the delta value, which has the greatest impact on current variation, is the highest for MOC with 2.71, followed by feed rate with 1.91, and cutting speed with the least effect of 0.39.
Figure 10 presents the main effect plot of cutting parameters on current. The S/N graph was plotted for the values taken according to the "smaller the better" principle. The highest values in the S/N graph indicate the lowest energy consumption. According to the graph, the optimum points are A3B1C1, which means MOC in Nano-MQL processing, a cutting speed of 150 m/min, and a feed rate of 0.1 mm/rev. MOC and feed rate have the most significant effect on the current, while the effect of cutting speed appears to be very weak. These results are in line with the studies by Jamil et al.[22]

3.5. Surface Roughness

To determine the surface quality of processed parts, the average surface roughness (Ra) index is generally used. The variation of surface roughness with respect to MOC, cutting speed V (m/min), and feed rate f (mm/rev) is shown in Figure 11. The effect of MOC on other parameters is clearly seen through all three graphs. It is well-known that surface roughness is theoretically a function of feed rate and tool tip radius. Generally, an increase in feed rate leads to an increase in surface roughness[36]. The best surface quality was achieved in Experiment 8, under cooling conditions with nano-MQL, 150 m/min cutting speed, and 0.1 mm/rev feed rate. This situation is particularly evident in the interaction between MOC and V as well as V and f. The lowest Ra values occur in this region of the curve. Similarly, Albayrak et al. have claimed that the most effective parameters for surface roughness are feed rate and spindle speed[28].
In MQL, nano-lubricants create an oxide film in the cutting zone to reduce friction, providing adequate lubrication. Nano-lubricants remove more heat from the heating zone. Therefore, the adhesion between the tool and workpiece material decreases, which helps to keep the cutting edges sharp and reduces cutting forces. The reduction in cutting forces directly implies reduced power consumption [37].
Nanoparticles cause a cushioning effect in the nano-lubricant. The cushioning effect absorbs sudden impacts by reducing fluctuations in cutting force. As a result, a decrease in surface roughness occurs[37].
The results indicate that MQL with nanoparticle-based lubrication reduces roughness values in different combinations of input parameters. Similar results are frequently encountered in the literature.
Dry cutting, MQL, and nano-MQL conditions have shown an increasing trend in surface roughness with increasing cutting speed. This may be related to an increase in cutting temperature with an increase in cutting speed, making it difficult to eliminate or reduce the generated cutting heat, resulting in tool wear and deterioration of the processed surface[14].
The calculated S/N ratios for the control factors used in the turning process regarding surface roughness are provided in Table 11. The analysis of the effect of each control factor on surface roughness was performed using the signal-to-noise ratio response table. Consistent with the literature, the factor with the largest delta value was f=4.405, making it the most significant parameter. MOC=3.758 was the second most important parameter, while V=0.258 was found to be of relatively low importance.
In Figure 12, the effects of cutting parameter levels on surface roughness were determined using the S/N ratio. When evaluating the S/N ratios for surface roughness (Ra), it is understood that the optimum cutting condition is A3B1C1. Since the slope of the line indicates the power of each control factor's effect, it is clear from the graph that the cutting speed has a very weak effect on roughness, while the most significant effect is with feed rate and MOC in the cooling method. It is also evident from the graph that the highest roughness value is achieved in dry cutting conditions and at the highest feed rate.

3.6. Sound Intensity

The level of noise generated during machining with chips is a complex issue influenced by various factors. These factors can arise from many variables such as the machining method used, material type, the condition of the cutting tool, machine type, and operating conditions. In machining with chips, the level of sound increases due to vibrations and noises generated during the cutting and shaping of the material by the cutting tool. The sound level during the process can vary depending on the type and size of the machine used.
Sound intensity was measured instantaneously during the experiments. The measured sound levels result from the interaction between the tool and the workpiece. Sound intensity undergoes an average change of approximately 2-3 dB under the same cutting conditions. Data were obtained at a frequency of 2 Hz with the measuring machine. The sound level associated with that specific experimental condition was determined by averaging all measurements. 3D graphs drawn based on the data obtained from the experiment results are shown in Figure 13. From all three graphs, it can be observed that the lowest sound intensity occurs under nano-MQL cutting conditions, with the lowest feed rate of 0.1 mm/rev and the middle level cutting speed of 150 m/min. The highest sound level, on the other hand, is observed when the feed rate is at its lowest and under nano-MQL conditions with the highest values for both cutting speed and feed rate. In their study, Albayrak et al. identified spindle speed, feed rate, and chip depth as the most effective parameters for sound level. They concluded that the spindle speed being identified as an important parameter was a result of the machine's inherent sound[28] .
From all the graphs, it is evident that the best results are obtained from the cooling model with nano-MQL. Other researchers have also confirmed the dominant role of nano-MQL in sound intensity[38,39].
The findings suggest that the increase in parameters such as tool wear, surface roughness, and current plays a role in increasing cutting sound[40]. This situation has been expressed in other studies as an increase in sound level leading to an increase in machining forces and surface roughness while reducing power consumption[41]. Similarly, an increase in surface roughness of processed parts and an increase in sound pressure levels in the environment have been observed as tool wear increases[42].
The analysis of the interaction of each control factor with sound intensity is shown in Table 13 in the S/N response table. Since the greatest difference between levels indicates the degree of interaction, MOC is the most important factor affecting sound intensity with a difference value of 0.73. Following MOC, feed rate with a difference value of 0.46 and cutting speed with a difference value of 0.09 are of relatively lesser importance. Again, according to the graph, the effects of the factors are very close in value.
Similarly, the S/N graph showing the interaction between sound intensity and control factors is shown in Figure 10. When evaluating the S/N ratios for sound intensity, it is observed that the optimum cutting conditions are A3B1C1. Considering the highest S/N ratios, it can be seen that the optimum levels are the 3rd level for MOC, the 1st level for cutting speed (100 m/min), and the 1st level for feed rate (0.1 mm/rev). It is apparent from the graph that MOC is a dominant factor in sound intensity. As lower sound levels are desired, it is seen that the lowest sound level is achieved under nano-MQL cooling conditions with the lowest cutting speed and feed rate, indicating that cutting speed has a weaker effect compared to other factors.
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Figure 10. S/N ratio for Sound Intensity (SI).
Figure 10. S/N ratio for Sound Intensity (SI).
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3.7. investigation of chip morphology

During machining with chips, a significant portion of mechanical energy is expended in this region due to the interaction between the workpiece and the tool. The structure of the chips formed during turning can provide important information about cutting processes. Chip morphology, in essence, refers to the physical properties of chips such as size, diameter, shape, and appearance[14]. In turning, chip morphology is primarily influenced by characteristics such as cutting parameters, tool-workpiece interactions, and cooling. These characteristics affect factors like material quality, cutting speed, tool life, and the surface quality obtained after machining. Therefore, correctly interpreting and understanding chip morphology is essential for a high-quality and efficient turning process.
The chips obtained from the experiments were examined under a microscope to gather information about chip formation mechanisms. Under dry cutting conditions, continuous chips were obtained at low feed rates, while an increase in the feed rate resulted in more segmented chips. As seen in the figure, under MQL and nano-MQL conditions, a reduction in curling radius and the formation of segmented chips are observed. This might be due to better lubrication in the sliding zone with MQL and the active role of nanoparticles in heat transfer under nano-MQL conditions.
As seen in the chip images, serrated chips were formed under all processing conditions investigated during the turning of AISI 4340. Although the serrated nature of chips varied depending on the cutting conditions, it is believed that this is primarily due to the relatively lower hardness of the material. Palanisamy et al.[18] explained this phenomenon as the localized chip formation due to the thermal softening of the material in the cutting zone. The serrations and instability in serrated chips were attributed to local thermal softening in the chips, resulting in significant deformation compared to adjacent materials.
The positioning and angle of the nozzle that delivers the MQL system to the cutting zone also significantly affect chip morphology formation. In the experiments, the nozzle was placed 20 cm away from the working area and directly over the chips based on literature. Similarly, in a study conducted, it was stated that this placement of the nozzle not only provided cooling but also lubrication, and it also reduced the formation of chips into small pieces, thus reducing the additional heat generated from friction between the tool and the workpiece[43].
When examining the chips as chip thickness from the images, it is observed that the highest chip thickness occurred in dry cutting, and the lowest chip thickness occurred in nano-MQL cutting. This situation occurs not only with an increase in cutting and feed rates but mainly in dry cutting due to the inability to remove the heat generated in the cutting zone, leading to thermal softening in both the workpiece and the tool. Chips produced in dry cutting are wider compared to those produced in MQL cutting. The wider chips in dry cutting are a result of the lateral flow observed in the cutting plane, as previously observed by the authors[44]. When MQL and nano-fluids are introduced, these coolants not only remove heat from the cutting zone but also create a film layer, reducing tool wear and changing the crater angle, leading to the formation of small broken chips. When examining the chip forms in the figure (exp. 1 V=100 m/min, f=0.1 mm/rev), it is seen that the curling radii of the chips decrease and chip thickness increases at low cutting speeds and high feed rates[40]. Similar results were found in a study by Khandekar et al.[1].
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Figure 14. Chip morphology under dry, MQL, and nano-MQL cutting conditions.
Figure 14. Chip morphology under dry, MQL, and nano-MQL cutting conditions.
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3.8. Confirmation Tests

In the experimental study, optimal results were obtained for tool wear, surface roughness, current, and sound intensity values, and the parameters that had an impact on the results were determined through variance analysis. The final step of the optimization process is to perform confirmation experiments and validate the optimization process. The parameter set that gives optimal values as a result of Taguchi optimization can sometimes be any of the existing experiments, while sometimes it may be an experiment conducted outside of the ones already performed. In the study, both Vb, Ra, I, and SI values reached the optimum result under the A3B1C1 experimental conditions, which was different from the existing experiments. Therefore, confirmation experiments were conducted under A3B1C1 (nano-MQL, V=100 m/min, f=0.1 mm/rev) conditions determined through Taguchi S/N optimization. The results obtained are compared with the results obtained from the experiments in Table 14.

4. Conclusion

In this study, AISI 4340 alloy steel was subjected to turning under different cooling conditions using PVD-coated tools. Cutting parameters' effects on tool wear, surface roughness, the current by the CNC, and ambient sound intensity were investigated in dry, MQL, and Nano-MQL cutting processes.
The results obtained from the experiments can be summarized as follows:
- The best values for all output parameters were achieved in experiment number 8 under the A3B2C1 conditions, with Vb=0.15 mm, Ra=0.81 µm, sound intensity of 88.1 dB, and a current value of I=4.18 A.
- The effects of cutting parameters on response variables were analyzed using the statistical method of ANOVA. The percentage contributions to the variation were as follows: MOC was the most influential factor with 55.97% for tool wear, 59.94% for current, 54.89% for surface roughness, and 67.27% for sound intensity. In this study, tool wear was modeled with an accuracy of 92.10%, current at 92.00%, surface roughness at 92.50%, and sound intensity at 95.87%.
- Flank wear was measured as tool wear using a microscope. Burn marks were generally observed on the cutting tools due to excessive heating. There was generally no sign of chipping or fracturing on the cutting edges, and uniform wear was observed in all tools. The highest wear occurred in experiment number 3, with Vb=0.420 mm, under dry cutting conditions with V=200 m/min and f=0.2 mm/rev, while the lowest wear was observed in experiment number 8, with Vb=0.150 mm, under nano-MQL conditions with V=150 m/min and f=0.1 mm/rev.
- Cutting parameters' effects on tool wear, current, surface roughness, and sound intensity were determined using the Taguchi S/N ratio. According to the S/N ratio, the optimum cutting conditions were found to be A3B1C1 for all output parameters. Since these experimental conditions were not part of the L9 series, confirmation experiments were conducted under nano-MQL cooling and V=100 m/min, f=0.1 mm/rev cutting conditions. The results of the confirmation experiment showed an improvement of 13% in tool wear, 7% in current, 9% in surface roughness, and 8% in sound intensity compared to the normal experimental results.
- When examining chip morphology, continuous chips with low feed rates and cutting conditions were obtained under dry cutting conditions, while with MQL and nano-MQL cooling, segmented chips with smaller radii were obtained at higher speeds.
- The use of Nano-MQL coolant resulted in the lowest values for all output parameters. In dry cutting conditions with high cutting speeds and feed rates, tool wear increased due to excessive heating in the cutting zone. An increase in tool wear led to higher values for sound intensity and surface roughness. As the required cutting force increased, the current also tended to increase. There was a positive relationship between surface roughness, tool wear, sound intensity, and current. When one of these four values increased, the others also increased.
In conclusion, Nano-MQL systems appear to have several advantages among the cooling/lubrication methods used in machining. However, during the experiments, it was observed that MWCNT, used as nanomaterial, had a tendency to adhere to all environments and atomize in the MQL spraying system, especially affecting the operator's health. For the sustainability of the necessary environmental impact conditions, future studies should pay attention to these issues. Along with the resolution of such problems, it is anticipated that the nano-MQL system will contribute to environmental friendliness, cleaner production, and the improvement of desired machinability properties.

Funding

This research received no external funding

Conflicts of Interest

“The authors declare no conflict of interest.”

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Figure 1. CNC lathe and work piece.
Figure 1. CNC lathe and work piece.
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Figure 2. Cutting Insert Dimensions.
Figure 2. Cutting Insert Dimensions.
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Figure 3. Schematic MQL System.
Figure 3. Schematic MQL System.
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Figure 4. Experimental setup of the study.
Figure 4. Experimental setup of the study.
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Figure 6. Microscopic images of tool wear produced in the turning by the Dry, MQL, and nano-MQL lubrication techniques.
Figure 6. Microscopic images of tool wear produced in the turning by the Dry, MQL, and nano-MQL lubrication techniques.
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Figure 7. 3D Graphs Illustrating the Effects of Cutting Parameters (MOC-V-f) on Tool Wear.
Figure 7. 3D Graphs Illustrating the Effects of Cutting Parameters (MOC-V-f) on Tool Wear.
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Figure 8. S/N ratio for Tool Wear (Vb).
Figure 8. S/N ratio for Tool Wear (Vb).
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Figure 10. S/N ratio for Current (I).
Figure 10. S/N ratio for Current (I).
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Figure 11. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Surface Roughness.
Figure 11. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Surface Roughness.
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Figure 12. S/N ratio for Surface Roughness (Ra).
Figure 12. S/N ratio for Surface Roughness (Ra).
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Figure 13. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Sound Intensity.
Figure 13. 3D Graphs Showing the Effects of Cutting Parameters (MOC-V-f) on Sound Intensity.
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Table 1. Chemical Composition of AISI 4340 (34CrNiMo6) steel.
Table 1. Chemical Composition of AISI 4340 (34CrNiMo6) steel.
Element C Mn P S Si Ni Cr Mo Fe
%Wt. 0.3-0.38 0.50-0.80 ≦0.025 ≦0.035 ≦0.40 1.3-1.7 1.3-1.7 0.15-0.30 Rest
Table 2. Mechanical Property.
Table 2. Mechanical Property.
Monotonik properties Symbol Value
Ultimate Tensile Strenght σUTS (Mpa) 1210
Yield Strenght σYs (Mpa) 1084
Rupture Strain A(%) 12,2
Reduction of The Area Z(%) 60,2
Young's Modulus E(Gpa) 210
Table 3. Process parameteres and their levels.
Table 3. Process parameteres and their levels.
Control Parameteres Notation Levels of factors
Level 1 Level 2 Level 3
Method of Cooling (-) MOC Dry MQL Nano-MQL
Cutting Speed -(m/min) V 100 150 200
Feed Rate -(mm/rev) f 0,1 0,15 0,2
Table 5. Expreimental result and S/N ratio.
Table 5. Expreimental result and S/N ratio.
Exp No MOC V f Vb S/N Vb Ra S/N Ra SI S/N SI I S/N I
1 1 100 0.1 0.241 12.36 1.35 -2.607 94.3 -39.5 5.13 -14.2
2 1 150 0.15 0.289 10.78 1.48 -3.405 98.7 -39.9 6.65 -16.4
3 1 200 0.2 0.42 7.53 2.12 -6.527 101.3 -40.1 7.21 -17.2
4 2 100 0.15 0.218 13.23 1.22 -1.727 95.2 -39.6 5.45 -14.7
5 2 150 0.2 0.27 11.37 1.91 -5.621 97.6 -39.8 5.64 -15.0
6 2 200 0.1 0.205 13.76 1.1 -0.828 93.7 -39.4 5.01 -14
7 3 100 0.2 0.217 13.27 1.36 -2.671 92.3 -39.3 5.11 -14.1
8 3 150 0.1 0.15 16.48 0.81 1.83 88.1 -38.9 4.18 -12.4
9 3 200 0.15 0.169 15.44 1.05 -0.424 90.1 -39.1 4.52 -13.1
Min 0.15 7.53 0.81 -6.527 88.1 -40.11 4.18 -17.2
Mak 0.42 16.48 2.12 1.83 101.3 -38.9 7.21 -12.4
Table 6. Variance Analysis (ANOVA) for Turning Responses.
Table 6. Variance Analysis (ANOVA) for Turning Responses.
Source DF PCR% Adj SS Adj MS F-Value P-Value
Tool Wear(Vb) MOC 2 55.97 0.029 0.029 35.41 0.002
V 2 4.55 0.002 0.002 2.88 0.151
f 2 31.58 0.016 0.016 19.98 0.007
Error 18 7.90 0.00403 0.00081
Total 26 100,00
S=0.0284 R2=92.10% R2(adj)= 87.35%
Current(I) MOC 2 59.94 4.472 4.472 37.48 0.002
V 2 2.46 0.184 0.184 1.54 0.270
f 2 29.60 2.208 2.208 18.51 0.008
Error 18 8.00 0.597 0.119
Total 26 100
S=0.03454 R-sq=92.00% R-sq(adj)= 87.21%
Surface Roughness (Ra) MOC 2 36.21 0.499 0.499 24.14 0.004
V 2 1.40 0.019 0.019 0.93 0.379
f 2 54.89 0.75615 0.75615 36.59 0.002
Error 18 7.50 0.10332 0.02066
Total 26 100
S=0.1437 R-sq=92.5% R-sq(adj)= 88.00%
Sound Intesity(SI) MOC 2 67.27 94.799 94.799 81.53 0.000
V 2 1.30 1.836 1.836 1.58 0.264
f 2 27.30 38.473 38.473 33.09 0.002
Error 18 4.13 5.814 1.163
Total 26 100
S=1.078 R2=95.87% R2(adj)=93.40%
Table 9. S/N ratio response for Current (I).
Table 9. S/N ratio response for Current (I).
Level MOC V f
1 -15.94 -14.37 -13.54
2 -14.58 -14.64 -14.76
3 -13.23 -14.75 -15.45
Delta 2.71 0.39 1.91
Rank 1 3 2
Table 11. S/N ratio response for Surface Roughness (Ra).
Table 11. S/N ratio response for Surface Roughness (Ra).
Level MOC V f
1 -4.180 -2.335 -0.535
2 -2.725 -2.399 -1.852
3 -0.421 -2.593 -4.939
Delta 3.758 0.258 4.405
Rank 2 3 1
Table 13. S/N Ratio response for Sound Intensity (S).
Table 13. S/N Ratio response for Sound Intensity (S).
Level MOC V f
1 -39.83 -39.45 -39.27
2 -39.6 -39.52 -39.52
3 -39.1 -39.55 -39.74
Delta 0.73 0.09 0.46
Rank 1 3 2
Table 14. Confirmation test results.
Table 14. Confirmation test results.
Vb I Ra SI
Exp. 8 (A3B2C1) 0,15 4,18 0,81 88,1
Exp. (A3B1C1) 0,13 3,85 0,73 81,3
% Improvment 13 7 9 8
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