Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool

: Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L 25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artiﬁcial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO 2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting ﬂuid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.


Introduction
Alloys have wide applications, such as for use as turbine and furnace components, and in the aerospace and petroleum industries [1]. Aluminum-based alloys have been most widely used in automotive industries due to their excellent formability [2]. There is a carbide. They found that the Taguchi method was a multiple performance optimization solver [26]. In an attempt to predict response parameters, Temel et al. [27] concluded that ANN is an effective method for predicting output variable with a minimum error for identifying mechanical and physical characteristics of Al 2 0 24 -B 4 C MMC. Bachy and Franke [28] used ANN and RSM to develop laser process mathematical models, and resulted in generating the lowest errors using an artificial neural network, which justified ANN's high precision response concerning surface methodology. Various researcher reported the use of artificial intelligence in the processing of Al-alloys [29][30][31]. Reddy compared RSM and ANFIS predicted surface roughness values, and the ANFIS results were superior to those of RSM [32]. A fuzzy model was developed using three different sets of process parameters and it was concluded that the ANFIS-based hybrid model was best suited to accurately predict surface roughness, with a MAPE value of 0.113542% compared to the developed ANN model [33].
Energy consumption evaluation is an integral part of life cycle assessment (LCA) and sustainability. Energy consumption varies with the machining stages and depends on the utilization of instruments assisting the machining [34,35]. The utilization of cooling and lubrication systems raised the power produced by the machine [36]. However, an analysis of electric energy usage is not sufficient to determine the sustainability of the machining process. Therefore, consumed resources' incarnated energy has been introduced into research in recent years [37,38]. Carbon (CO 2 ) emission and carbon footprint analysis is also a key indicator of LCA and sustainability. Ic et al. [39] optimized the processing parameters of a machine to turn Al-7075 alloy to minimize the emission of CO 2 and to produce a low surface roughness. It was reported that the proper selection of coolant reduced the generation of CO 2 emissions and was better for the environment. CO 2 emissions have been found to be approximately zero in the MQL and cryogenically assisted machining process. Production costs are also essential for LCA and sustainability. The production costs of machined components were computed using Taylor's cost method. The model comprised tool cost, machine cost, and tool change cost, but this model does not include a coolant effect on the production costs [40]. Jamil et al. proposed a cost model for dry, MQL, and LN 2 -assisted cooling of machining of Ti-alloy, and it was found that the production cost was reduced significantly [41].
The necessity for environmentally friendly machining has always been the motivating factor regarding cutting-fluid-usage reduction techniques. MQL is amongst the most promising machining strategies, allowing for a 90% reduction in cutting fluid consumption, although it also preserves surface quality and tool life. The prominence of MQL in machining necessitates an analysis of MQL progress. During machining operations, flood cooling is a popular method for extracting heat from the cutting field; however, it has negative consequences, such as it can cause respiratory illnesses among employees, environmental issues, and cutting fluid processing and disposal. As a result, environmentally sustainable lubrication solutions must be implemented to address flood lubrication issues. The MQL strategy can also help with machining issues like chip pressure welding to the cutting edge, which is the main reason for tool failure. Additionally, MQL improves the surface quality of machined parts. However, MQL is mainly concerned with reducing friction, rather than removing as much heat as possible by regulating heat generation at its source.
A detailed literature survey has revealed that there is a complete dearth of literature on life cycle assessment (LCA) and sustainability analyses of MQL-assisted machining of Al-Mg-Zr alloy. The sustainability aspect of the machining area requires a great deal of attention and needs to be comprehended in a structured manner in comparative studies. Unfortunately, most academics and manufacturing industries are still focused only on traditional methods. However, insincere efforts have been made in understanding the effects of environmental, economic, and technological analyses of MQL-assisted machining of Al-Mg-Zr Alloy. The present study has created another endeavor, to fill the gap due to the already-stated theoretical research gaps. The current research on MQL-assisted machining of Al-Mg-Zr alloy using a PCD tool has created the following objectives:

•
To determine the influence of cutting parameters on sustainable responses, viz. surface roughness and cutting temperature using ANOVA and the main effect of plots using the PCD tool; • To achieve optimal cutting parameters with the Taguchi method and multi-objective optimization with desirability function analysis (DFA); • To model cutting parameters with ANN and ANFIS; • To analyze life cycle and sustainability aspects of MQL-assisted machining of Al-Mg-Zr alloy.
To understand the extent of sustainability analysis on MQL-assisted machining of Al-Mg-Zr alloy, the present study was based on a fundamental analysis of three research questions: Q1: Do cutting parameters significantly affect sustainable responses or not? Q2: Do the cutting parameters differ from optimal settings? Q3: Does a statistically significant interrelationship between parameters contribute to "sustainable machining"?
The rest of the paper is organized as follows: Section 2 details the materials and methods utilized for modeling and optimization, workpiece material, equipment and the instruments used as well as the design of the experiments. Section 3 describes the analysis of results using ANOVA, optimization with the Taguchi method, and multi-objective optimization with DFA, ANN, and ANFIS modeling. Section 4 displays a comparative analysis, including ANN and ANFIS results and life cycle and sustainability analysis, followed by conclusions, limitations, and the future scope of research, in Section 5.

Workpiece Material and Dimensions
Aluminum, magnesium, and zirconium were used in this work. A stir casting process with an RPM of 600 was used to prepare the alloy. EDX analysis was carried out to obtain the developed alloy composition, as shown in Table 1 and Figure 1. A cylindricalshaped alloy was prepared for turning operations, with a length of 305 mm and a diameter of 70 mm.

Cutting Inserts
An SNMG 120408 polycrystalline diamond (PCD) cutting insert with a nose radius of 0.8 mm under minimum quantity lubricant (MQL) cutting conditions was used in the turning process using a center lathe.

Equipment and Instruments
The roughness was measured using an optical surface profilometer. Each measurement was repeated three time to reduce the error and the mean arithmetic average value was collected.
During the experiments, a thermocouple was used to monitor the cutting temperature. In addition, a digital multi-meter was used to record the electromotive force (emf) in millivolts, with one end of the multi-meter attached to the workpiece and the other end to the tool. The thermocouple calibration maintained the temperature measuring accuracy using comparison techniques. The calibration procedure was achieved according to the ASTM E220 standard test method by trained lab technicians [42].

Cutting Inserts
An SNMG 120408 polycrystalline diamond (PCD) cutting insert with a nose radius of 0.8 mm under minimum quantity lubricant (MQL) cutting conditions was used in the turning process using a center lathe.

Equipment and Instruments
The roughness was measured using an optical surface profilometer. Each measurement was repeated three time to reduce the error and the mean arithmetic average value was collected.
During the experiments, a thermocouple was used to monitor the cutting temperature. In addition, a digital multi-meter was used to record the electromotive force (emf) in millivolts, with one end of the multi-meter attached to the workpiece and the other end to the tool. The thermocouple calibration maintained the temperature measuring accuracy using comparison techniques. The calibration procedure was achieved according to the ASTM E220 standard test method by trained lab technicians [42].

Experimental Setup and Design of Experimentation
Experiments were performed according to Taguchi's methodology [43]. The cutting parameters, such as cutting speed (Vc), feed rate (S0), and cutting depth (t), using five different levels, were considered. The experiments were conducted to decide the cutting parameter levels along with the tool manufacturing suggestions and the machine tool capacity using an L-25 OA array. The optimization was performed using desirability function analysis, as reported in previous research [44]. The cutting parameters and the allocation of the different levels are shown in Table 1. The standard L25 OA as per the cutting parameters levels is shown in Table 2. Each experiment was repeated three times as designed, and the average values of the responses for surface roughness (Ra) and cutting temperature cutting (θ) are shown in Table 3. Figure 2 presents the experimental setup and the method used for the study. The pressure was kept at 8 bar, and the flow rate during machining was kept constant at 120 mL/h.

Experimental Setup and Design of Experimentation
Experiments were performed according to Taguchi's methodology [43]. The cutting parameters, such as cutting speed (V c ), feed rate (S 0 ), and cutting depth (t), using five different levels, were considered. The experiments were conducted to decide the cutting parameter levels along with the tool manufacturing suggestions and the machine tool capacity using an L-25 OA array. The optimization was performed using desirability function analysis, as reported in previous research [44]. The cutting parameters and the allocation of the different levels are shown in Table 1. The standard L 25 OA as per the cutting parameters levels is shown in Table 2. Each experiment was repeated three times as designed, and the average values of the responses for surface roughness (R a ) and cutting temperature cutting (θ) are shown in Table 3. Figure 2 presents the experimental setup and the method used for the study. The pressure was kept at 8 bar, and the flow rate during machining was kept constant at 120 mL/h.

ANFIS and ANN Based Predictive Modelling
The adaptive fuzzy inference system (ANFIS)-based network is a hybrid architecture comprising neural and fuzzy logic systems. An information-based technique is used in a neural network stage to provide function-approximation problems [33]. First, a fuzzy inference method with an initial fuzzy model is established, taking into account the fuzzy rules extracted from the input-output data. Then, the neural network is used to fine-tune the built-up original fuzzy model's rules in the next step.
A Sugeno (output is linear or constant) type ANFIS model is used in the present work. The experiment takes three input parameters (depth of cut, cutting speed, and feed rate) and two output parameters (surface roughness and cutting temperature) into consideration. The ANFIS model is constructed using a Gaussian membership function (Gaussmf) with three membership functions for all input parameters and a linear membership function for the output parameter. The hybrid approach is used to maximize parameter performance.
ANN is a common prediction tool that helps to calculate various outputs from different parameters. ANN has three interconnected layers, and core features of the artificial neural network are its learning ability and the various learning algorithms [33]. To get a minimal variance between the experimental values and the output values, it is essential to decide the best learning algorithm and the optimum number of neurons.

Data Collection for Sustainability Analysis
PPC-3 power meter load controls were utilized to calculate energy consumption during different phases of machine tooling. In order to obtain the electrical energy consumption in various process stages (functional states of the machine tool), the estimated power was compounded by a known time frame. In the measurement of the lubrication capacity, a smart meter was used. The intelligent meter consisted of Raspberry Pi-3 and a Smart-Pi, keyboard and mouse, an HDMI adapter and a micro SD card. In addition, the proposed uniform approach calculates carbon emissions, as reported in a previous study [45].

Effect of Process Paramters on Surface Roughness and Cutting Temprature
An SN ratio shows the influence of each input variable parameter on each regulated parameter level, and the parameter has an impact on the surface roughness and cutting temperature. Since a higher SN is closer to a higher quality, a bigger SN ratio is better. Both Tables 4 and 5 demonstrate that cuts at speeds ranked 1, followed by cutting depth rank 2, had the least impact on the results and the feed rate, as the cutting parameter affected the surface roughness most effectively and the cuts in the temperature quality were in accordance with the SN ratio.  Figures 3-6 correspondingly show the principal effect parts for the mean and SN ratios of surface roughness and cutting temperature. It is usually preferable to increase the value of the SN ratio; hence, the ideal cutting conditions can be attained when the SN ratios are highest in all three entry parameters. The primary SN ratio effect plot (Figures 3 and 5) shows that a low roughness and cutting temperature reached at a 0.25-mm cutting depth, 86-m/min cutting speed and a feed-rate of 0.12 mm/rev. Figures 4 and 6 show that the ideal value at a cut-through depth of 0.25 mm, 86 m/min cutting speed and 0.12 mm/rev feed rate was seen for reduced surface roughness and cutting temperature. In order to get the optimum assessment of both surface roughness and cutting temperature, it was proposed that a lower cutting depth, lower cutting speed and low-to-moderate feed rate need to be used. 3 and 5) shows that a low roughness and cutting temperature reached at a 0.25-mm cutting depth, 86-m/min cutting speed and a feed-rate of 0.12 mm/rev. Figures 4 and 6 show that the ideal value at a cut-through depth of 0.25 mm, 86 m/min cutting speed and 0.12 mm/rev feed rate was seen for reduced surface roughness and cutting temperature. In order to get the optimum assessment of both surface roughness and cutting temperature, it was proposed that a lower cutting depth, lower cutting speed and low-to-moderate feed rate need to be used.

Analysis of Variance (ANOVA)
In this study, ANOVA was performed for the response of surface roughness and cutting temperature. The sum of the squares (SS), the degrees of freedom (df), the mean square (MS), the F-value, and the p-value for all input variables are shown in Tables 6 and  7 for the MQL-aided cutting conditions. p-values below 0.05 indicate that the terms of the model are noteworthy for both responses. Moreover, it is easily understood from the anal-

Analysis of Variance (ANOVA)
In this study, ANOVA was performed for the response of surface roughness and cutting temperature. The sum of the squares (SS), the degrees of freedom (df), the mean square (MS), the F-value, and the p-value for all input variables are shown in Tables 6 and 7 for the MQL-aided cutting conditions. p-values below 0.05 indicate that the terms of the model are noteworthy for both responses. Moreover, it is easily understood from the analyses that the cutting speed and the depth of the cut are the most prominent factors, which is aligned with the results of the S/N ratio analysis. Thus, both the method factors can give an assertion about selecting the dominant factor affecting the process.

Optimization Using Desirability Function Analysis (DFA)
Multi-response optimization aims to determine the independent variable conditions that lead to optimal or nearly optimal values for the response variables. In this desirability function analysis, minimizing surface roughness and cutting force was the prime objective. Factor ranges defined for the basic optimization are shown in Table 8. A summary of the optimization to fulfil the essential purpose of minimizing the response parameter is demonstrated in Table 9 and Figure 7.

Proposed ANN Model
Various learning algorithms, such as LM, CGP, SCG and BFG, are used to determine the most appropriate learning algorithm for predicting surface roughness and cutting temperature. The number of hidden layers were modified to 9, 12, 15, and 20 to achieve the best possible proximity to the experimental results. For an increasing number of hidden layers, the response parameter values were calculated using the correlation coefficient (R 2 ) comprising all four types of learning algorithms. Of all the algorithms, the LM algorithm was best suited for the training data of the surface roughness and cutting temperature. At the same time, SCG was the most suitable algorithm for testing data collection. When testing with cutting temperature experimental data collection, barring the best network structure for checking the cutting temperature data, i.e., 3-15-2, the best network structure for the remainder of the data set was 3-9-2, as shown in Figure 8. The proposed ANN structure showed that it has three neurons (depth of cut, cutting speed, and feed rate) in the input layer, nine neurons in the hidden layer, and two neurons in the output layer (surface roughness and cutting temperature). In Table 10 In this study, 25 experimental data points were arranged for training and testing purposes. Thus, 72% of the data was used for training purposes, while 28% of the data was used for testing purposes; 18 datasets were used for training, and the remaining 7 datasets were used for testing.

ANFIS Based Predictive Modeling
The hybrid technique is used to examine the test dataset by loading it from the workspace into ANFIS. For all input parameters and linear type membership functions for output membership functions, FIS was formed using the Gaussian mission function Gaussmf and was shown in Figure 9. The hybrid technique was chosen as the 0 percent error tolerance and 100 epoch optimization approach was used until there was a minimum error in a correct result. Membership function and rules generated by FIS for both the outputs are shown in Figures 10 and 11. The corresponding errors obtained for the surface roughness and the cutting temperature in the hybrid method were 1.107% and 1.172%, respectively, using the rule viewer, as shown in Figure 11. With this changing of the inputs, the resultant output values for the developed hybrid-type ANFIS model was generated.   In this study, 25 experimental data points were arranged for training and testing purposes. Thus, 72% of the data was used for training purposes, while 28% of the data was used for testing purposes; 18 datasets were used for training, and the remaining 7 datasets were used for testing.

ANFIS Based Predictive Modeling
The hybrid technique is used to examine the test dataset by loading it from the workspace into ANFIS. For all input parameters and linear type membership functions for output membership functions, FIS was formed using the Gaussian mission function Gaussmf and was shown in Figure 9. The hybrid technique was chosen as the 0 percent error tolerance and 100 epoch optimization approach was used until there was a minimum error in a correct result. Membership function and rules generated by FIS for both the outputs are shown in Figures 10 and 11. The corresponding errors obtained for the surface roughness and the cutting temperature in the hybrid method were 1.107% and 1.172%, respectively, using the rule viewer, as shown in Figure 11. With this changing of the inputs, the resultant output values for the developed hybrid-type ANFIS model was generated. In this study, 25 experimental data points were arranged for training and testing purposes. Thus, 72% of the data was used for training purposes, while 28% of the data was used for testing purposes; 18 datasets were used for training, and the remaining 7 datasets were used for testing.

ANFIS Based Predictive Modeling
The hybrid technique is used to examine the test dataset by loading it from the workspace into ANFIS. For all input parameters and linear type membership functions for output membership functions, FIS was formed using the Gaussian mission function Gaussmf and was shown in Figure 9. The hybrid technique was chosen as the 0 percent error tolerance and 100 epoch optimization approach was used until there was a minimum error in a correct result. Membership function and rules generated by FIS for both the outputs are shown in Figures 10 and 11. The corresponding errors obtained for the surface roughness and the cutting temperature in the hybrid method were 1.107% and 1.172%, respectively, using the rule viewer, as shown in Figure 11. With this changing of the inputs, the resultant output values for the developed hybrid-type ANFIS model was generated.

Three-Dimensional Surface Plots
To understand the interaction effects of the input variables on the response factor, 3D surface plots were plotted based on a developed ANFIS-based model, as portrayed in Figure 12. From the surface views, it was found that a three-dimensional curve represented the mapping from any of the two input parameters to the output parameter. The Z-axis represents surface roughness and the cutting temperature, while the X and Y axes represent two input parameters. All the input parameters could be changed to see how they affected the output parameter. The surface plot displays the effect of the feed rate on Ra and θ is negligible compared to the impact of the cutting speed and the depth of cut. This demonstrates that the increase in cutting speed and depth of cut leads to the rise of Ra and θ. However, in the case of cutting speed, sudden blips are also noticeable for the output parameters. Conversely, when large values of cutting speed were used, the effect became important. Therefore, it can be concluded that cutting speed exhibited the maximum influence on the response variable (Table 5).

Three-Dimensional Surface Plots
To understand the interaction effects of the input variables on the response factor, 3D surface plots were plotted based on a developed ANFIS-based model, as portrayed in Figure 12. From the surface views, it was found that a three-dimensional curve represented the mapping from any of the two input parameters to the output parameter. The Z-axis represents surface roughness and the cutting temperature, while the X and Y axes represent two input parameters. All the input parameters could be changed to see how they affected the output parameter. The surface plot displays the effect of the feed rate on R a and θ is negligible compared to the impact of the cutting speed and the depth of cut. This demonstrates that the increase in cutting speed and depth of cut leads to the rise of R a and θ. However, in the case of cutting speed, sudden blips are also noticeable for the output parameters. Conversely, when large values of cutting speed were used, the effect became important. Therefore, it can be concluded that cutting speed exhibited the maximum influence on the response variable (Table 5).
affected the output parameter. The surface plot displays the effect of the feed rate on Ra and θ is negligible compared to the impact of the cutting speed and the depth of cut. This demonstrates that the increase in cutting speed and depth of cut leads to the rise of Ra and θ. However, in the case of cutting speed, sudden blips are also noticeable for the output parameters. Conversely, when large values of cutting speed were used, the effect became important. Therefore, it can be concluded that cutting speed exhibited the maximum influence on the response variable (Table 5).
(a) (b) Figure 12. Variation of (a) surface roughness and (b) cutting temperature on different combinations of inputs (depth of cut, cutting speed, and feed rate). Figure 12. Variation of (a) surface roughness and (b) cutting temperature on different combinations of inputs (depth of cut, cutting speed, and feed rate).

Comparison of Experimental Data with ANN and ANFIS Predicted Model
Mean absolute percentage error is used to measure the error of the process and was defined as Equation (1). MAPE calculation for the predicted values is shown in Table 11.
where APE = tj−oj tj × 100; t is the target value, o is the output value, and P is the number of samples.
The errors between the experimental and predicted values of R a and θ are 3.95% and 3.45% when using a model based on ANN. Subsequently, the hybrid prediction model based on ANFIS was used to detect the deflection of the expected values from the experimental values to verify the percentage of process errors. MAPE was 1.072% and 1.172% for surface roughness and cutting temperature when predicting using the ANFIS model. It could be concluded that the hybrid method predicted the resulting output much more accurately since the amount of deviation was minimal compared to the predicted values of ANN. Nonetheless, ANN could also reasonably predict this value. Figures 13 and 14 show the disparity between the measured and predicted responses of R a and F z , which indicates the model's efficacy, as the predicted values for various combinations were closer to those of the experimentally reported readings.

Life Cycle Assessment and Sustainability Analysis
In the present research study, the empirical models for calculating production time, energy efficiency, specific cutting energy, and carbon emission were developed using a bottom-up approach, as presented in previous research [43]. Here, LCA and sustainability performance analysis, as per the design of the experiment, was presented in terms of production time, energy efficiency, specific cutting energy, and carbon emission. Figure 15 shows the LCA and sustainability performance outputs for dry and MQL-assisted machining of Al-based materials using the PCD tool.

Life Cycle Assessment and Sustainability Analysis
In the present research study, the empirical models for calculating production time, energy efficiency, specific cutting energy, and carbon emission were developed using a bottom-up approach, as presented in previous research [43]. Here, LCA and sustainability Every operation required a determined time to operate the machining process, called the "actual time" (Tam). Therefore, the total processing time included the idle time, air cutting time, cutting time, tool change time, and cooling/lubrication time, as calculated in Equations (2) and (3).
Processing time (T c ) = t i + t a + t c + t tc + t col lub (2) The idle time is the sum of stand-by time, material handling, and setup time.
The total processing time for each experimental run after calculation is shown in Figure 15a. It can be seen that MQL-assisted machining takes less time for cutting job operation in comparison to dry-machining. The minimum process time was determined at the optimized setting and was 195 s. This was due to the tool having a longer tool life, which reduced the tool change time. In addition, the MQL cutting fluid helped to reduce the cutting temperature, dissipate heat from the machining zone, and improve the tool's efficiency and life. Similar observations have been made in previous research work [4,46]. The machining energy efficiency (η) was also considered an attribute for LCA and sustainability and is computed as shown in Equations (4)- (6).
Machining energy e f f iciency (η) = Machining Energy (E m ) Total Energy Consumed (E T ) (4) where power consumption can be calculated by various stages of the machine tool, as shown in Equation (5).
Machine Power (P m ) = P i t + P a t + P c t + P tc t + Pcol lub t (5) performance analysis, as per the design of the experiment, was presented in terms of production time, energy efficiency, specific cutting energy, and carbon emission. Figure 15 shows the LCA and sustainability performance outputs for dry and MQL-assisted machining of Al-based materials using the PCD tool. Every operation required a determined time to operate the machining process, called the "actual time" (Tam). Therefore, the total processing time included the idle time, air cutting time, cutting time, tool change time, and cooling/lubrication time, as calculated in Equations (2) and (3).
The idle time is the sum of stand-by time, material handling, and setup time. Energy consumption is expressed in Equation (6):  Figure 15b shows the machine energy efficiency in terms of the experimental run for dry and MQL-assisted machining. It can be seen that the machine energy efficiency varies in the range of 20-34%. Thus, the reduction in energy consumption and the reduction of waste energy in machine tools is not the only way to achieve a high energy efficiency. Thus, the primary objectives were to reduce energy consumption and waste generation. The results show that MQL-assisted machining has a high energy efficiency compared to dry machining because the tool life is longer in MQL-assisted machining, which reduces tool change. This further reduced stand-by energy utilization in the tool changing phase.
Specific cutting energy (SCE) was also one of the key indicators of sustainability analysis. SCE is the ratio of total energy consumed to the material removal rate [47]. To reduce the SCE, either the material removal is increased using the same energy or a reduced energy consumption is needed for the same material removal, as presented in Equation (7).
Material removal rate (MRR) is calculated as per Equation (8). Figure 15c shows the SCE concerning the experimental run for dry and MQL-assisted machining. It can be seen that the SCE varies in the range of 2-4 J/mm 2 . From the results, it can be seen that MQL-assisted machining has a low SCE compared with dry-machining. The prime reason for this is dependent on the sharpness of the cutting edge of the tool. In the case of MQL-assisted machining, the tool cutting edge has a long-life cycle. Therefore, the tool's life depends on its cutting edge sharpness, which requires a low cutting force and energy to perform job operations. Moreover, there is no built-up-edge formation in the process, which increases the tool life and maintains the cutting edge's sharpness because MQL-lubrication disposes of the heat energy generated in the machining zone. As a result, the long-life sharp edge produced a high material removal, and less tool change, which reduced cutting power consumption, which reduced the SCE.
Carbon emissions (CO 2 ) can be termed as another significant indicator of sustainability. The energy consumption indirectly calculates carbon emissions via machine tools. The total carbon emission (CE total ) is calculated as per Equation (9). Figure 15d shows the variations in CO 2 concerning experimental run for dry and MQL-assisted machining. The carbon emissions under the obtained sustainable machining process were in the range of 200 to 300 kg-CO 2 . However, it can be seen that the CO 2 emissions were comparatively much lower in MQL-assisted machining as compared to dry machining. This is because, in MQL machining, a very nominal amount of cutting fluid is used, which generates a smaller carbon footprint. Moreover, less energy consumption is required in MQL machining to achieve high degree of material removal and a very low tool change. In this, savings in energy, time, money, as well as for the environment occur, making MQL a sustainable machining process [43,[48][49][50].
The obtained results are in line with previously published research work. Khan et al. reported investigating Al2O3-based nano-fluid-assisted MQL machining in terms of technological and economic factors [51]. Various sustainable parameters, such as energy, cost, and carbon emission were studied. It has been reported that nano-fluid-assisted MQL machining reduces the cost per unit of the product and achieves a low carbon manufacturing goal [52]. Gupta et al. studied the environmental and sustainability aspects of hybrid-cooling-assisted machining of Ti-6Al-4 V alloy [53]. It was reported that LN 2 and MQL were the most effective cooling systems and helped in reducing the production time, energy, cost, and carbon emissions. Similar to the above-mentioned studies, the current study also presented that MQL machining reduces production time, energy, cost, and carbon emissions while processing Al-based alloys. Other sustainability indices, such as human wellbeing, pollution, vibrations, etc., will be considered in future research.

Conclusions
Current research focuses on the sustainability aspect of machining Al-based alloy using the PCD tool. At the same time, various machining attributes (surface roughness and cutting temperature) were also addressed to interrelate the effect of input parameters on the resultant responses. The addition of Zr in the material preparation significantly impacted reducing the cutting temperature under MQL cutting conditions. Based on the research, the following conclusions can be made.
From ANOVA analyses, it is understandable that the cutting velocity was the most dominant factor, followed by the depth of cut, to induce a preferable value for both the response parameters, which was also aligned with the SN ratio results attainted. As such, both methods can give assurances regarding the identification of the dominant factors affecting the response variables.
The SN ratio response graph demonstrates that, to obtain the optimum surface roughness and cutting temperature, a combination of parameters with a depth of cut of 0.25 mm, cutting speed of 86 m/min, and feed rate of 0.10 mm/rev was desirable. On the other hand, DFA showed the optimum surface roughness and cutting temperature, with a combination of 0.305 mm for the depth of cut, 86 m/min for cutting speed, and a 0.10 mm/rev for feed rate. Moreover, the highest perceived value of 0.997 could be attained amongst the 92 solutions while setting the mentioned parameters.
The ANN-based model effectively established a link between the input factors and response variables for the testing dataset and the training dataset. It could also be observed that the mean absolute percentage error fell to a good range of values of 3.25% to 3.95% for the response variables. The deviation chart indicated the acceptability of the Sugeno hybrid ANFIS prediction model, with mean absolute percentage errors of 1.071% and 1.72%, respectively, for surface roughness and cutting temperature, demonstrating the capacity to predict the performance of the responses more reliably than the ANN model.
MQL-assisted machining has been proven to be a sustainable machining process as compared to dry-machining. In MQL machining, owing to the very nominal use of cutting fluid, it generates a smaller carbon footprint. Moreover, significantly less energy consumption is required in MQL machining to achieve a high material removal and very low tool changes were needed. In this, there are savings in terms of energy, time, money, and the environment, making MQL a sustainable machining process.
It appears that the minimum quantity of lubrication could play a prominent role in maintaining the sustainability aspect of machining of the chosen material. Nevertheless, before applying this method for frequent industrial applications, more research needs to be done on the machining behavior considering higher cutting speeds and various nose radii of the PCD tool.
A limitation of current research work is that other sustainability indexes, such as human wellbeing, pollution, vibrations, etc., are not covered and will be considered in future research work.