# Analysis of CSN 12050 Carbon Steel in Dry Turning Process for Product Sustainability Optimization Using Taguchi Technique

Authors: Melesse Workneh Wakjira; Holm Altenbach; Janaki Ramulu Perumalla
Journal: Journal of Engineering
 (2019)

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## Abstract

The aim of this research paper is to investigate the machinability of CSN 12050 carbon steel bars using carbide insert tool in order to utilize the optimum cutting parameters by employing Taguchi approach. Experiments have been performed under dry cutting condition using an optimization approach according to Taguchi’sL9(34) orthogonal arrays; signal-to-noise ratio tests are designed. Analysis of variance (ANOVA) was performed to determine the importance of machining parameters on the material removal rate (MRR). The results were analyzed using signal-to-noise ratios (S/N); 3D surface graphs, main effect graphs of mean, and predictive equations are employed to study the performance characteristics. The optimal parameters resulted as A3B2C3 (i.e., cutting speed 275 (m/min), depth of cut 0.35 (mm), and feed rate 0.25 (mm/rev), respectively). In the present study, there is an improvement of 5.22 dB at optimal cutting conditions for each significantMRR response parameters such as cutting speed, depth of cut, and feed rate. With these proposed optimal parameters, it is possible to optimize machinability for product sustainability.


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## Body

## 1. Introduction

Turning process is the most common method for metal cutting and especially for the finishing machined parts in the manufacturing industries. Optimization techniques application in turning process is an essential for increasing quality of product and to sustain competitiveness in the manufacturing industries. In today’s scenario, the furthest significant thing which is considered in manufacturing industries is the value of a product.The value of a product can be increased by either improving the quality of the products or reducing the cost, environmental, and societal impacts. Surface finish (Ra) is the most critical measures for quality, performance, and service life of the mechanical products and it is a good parameter to evaluate the sustainability of the machined products [1–4].In turning operations, reducing power consumption and maximizing the material removal rate (MRR) could influence the performance of mechanical components and the production costs in the manufacturing industries [5–8]. In order to improve the machining conditions, effort is required to minimize the value of power consumption and maximize the value of MRR by selecting optimal turning process parameters such as cutting speed (Vc), depth of cut (Cd), and feed rate (fr) [9–12]. Taguchi’s method can be applied for optimization of the process parameters to produce high quality products that can encounter the goal of product sustainability [13–15].On the bases of literature review, it had found that a substantial amount of work was carried in relation toRa, MRR, and power consumption with parametric optimization through modeling, simulation, and design of experiment (DOE) using Taguchi method. Nevertheless, no work was found in analysis of turning process and in competitiveness with performance parameters for product sustainability optimization using Taguchi techniques on CSN 12050 carbon steel. The present research work shows the detailed plan of experimentation through scientific methodology based on Taguchi design of experiments.The main objective of the present work is to investigate turning process parameters which will give maximum material removal rateMRR using the CNC lathe turning operations on CSN 12050 carbon steel workpiece material. The aim of the research of this paper is to investigate the machinability of CSN 12050 carbon steel in order to utilize the optimum cutting parameters using Taguchi method. The paper is organized as follows: an overview of Taguchi method and experimental aspect,DOE, and experimental setup using the parameter design to determine and analyze the optimal cutting parameters in turning operations. Product sustainability has been taken into account by specifying dry cutting condition and identifying the optimal of cutting parameters. Consequently, sustainability can be achieved from economical, societal, and environmental aspects as illustrated in Figure 1. Finally, the paper concludes with a summary of this research work based on the analysis of experimental results.Figure 1
Pillars of product sustainability and the corresponding benefits.


## 2. Taguchi Method and Experimental Aspects

Taguchi method provides the practical engineer with a useful starting point for quality improvement. The traditional experimental design method is more focused on the statistical aspects whereas the Taguchi’s method is primarily focused on the engineering aspects of quality. Taguchi has developed a methodology for the application of design of experiments, including practitioner’s handbook [14, 15]. This methodology has taken the design of experiments from the exclusive world of the statistician and brought it more fully into the world of manufacturing [13]. Taguchi introduces his approach, using experimental design [14] for designing and developing products/processes.Taguchi method is encouraged due to its desirability of the application that can incorporate the statistical methods into the authoritative engineering process. In Taguchi method, the main parameters have influence on process results, which are positioned at different rows in a designed orthogonal arrays (OAs). The difference between the functional value and objective value is recognized as the loss function that can be expressed by signal-to-noise (S/N) ratio. Taguchi recommends the use of the loss function to measure the performance characteristic deviating from the desired value. The value of the loss function is further transformed into a signal-to-noise (S/N) ratio (η). Usually, there are four categories of the performance characteristics in the analysis of the S/N ratio, that is, the higher-the better, the lower-the better, the nominal-the better, and fraction-defective [9, 14]. Hence, the higher-the better performance characteristic is used for this research work. TheS/N ratio for each level of process parameters is computed based on theS/N analysis. Furthermore, a statistical analysis of variance (ANOVA) is performed to see which process parameters are statistically significant. With theS/N andANOVA analysis, the optimal combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design. In this research paper, the cutting parameter designed by Taguchi method is adopted to obtain factors which influence the optimal machining performance (response) in turning process [15].The quality characteristic used for the analysis of S/N ratio(η) [9, 15] is expressed as(1)Larger-the  better,η=-10log⁡1n∑i=1n1Yi2wheren is number of trial /observations and Yi is measured value of quality characteristics for ith, trial condition. Notice that these S/N ratios are expressed on decibel scale.The goal of this research was to produce maximized material removal rate (MRR) in turning operation. Larger MRR values represent better or maximized material removal rate. Therefore, the larger-the better quality characteristic was implemented and introduced in this study. The quality characteristic is continuous and nonnegative (0 to ∞). The ideal target value of this type quality characteristic is ∞ (as larger as possible) [15].The following equations used to calculate the weight of material removed, with the required cycle time to remove material, measured ingm/min, ormm3/min [8, 16], are expressed as (2)Wi=V×ρwhere Wi weight of material before machining, V volume of the machined workpiece material, and ρ is density of workpiece material(3)Wf=V1+V2×ρwhere Wf is weight of material after machining(4)tc=tf-tiwhere tc is cycle times to remove the material, ti is starting time, and tf is finishing time(5)MRRgm/min=MRwtcwhere MRw is weight of material removed(6)MRRmm3=MRRgm/minρ(7)MRR=Wi-Wfρs×tcmm3/minThe pillars in Figure1 show the achievement of profit and economical aspect through cost minimization: procurements cost, operating costs, reduced use of cutting fluids, maintenance cost, and productivity improvement as a result of optimized cutting parameters. The success of people and societal aspects shows how to maintain occupational health by avoiding an aerosol which exists in the form of mists, due to the interaction of the cutting fluids with the machine tools and workpiece materials. Correspondingly, the possibility of our planet and environmental wellbeing by reducing the disposal of used cutting fluids, power consumption required for cutting fluids pump motor, airborne emission, and water pollution. The overall benefits of product sustainability achievements shown in Figure 1 pillars through dry turning conditions for CSN 12050 carbon steel summarized the ultimate goal of this study.


### 2.1. Design of Experiments

“Experimental Design (ED) is a strategy of planning, conducting, analyzing, and interpreting experiments so that sound and valid conclusions can be drawn efficiently, effectively, and economically” [14, 15]. Design of Experiment (DOE) with Taguchi is divided into three main phases which are planning, conducting, and analysis phase [15]. The planning phase is the most important phase for experiment to provide the expected information. Sometimes the information from the experiment is in positive or negative sense. Positive information is an indication of which factor and which level lead to improving product or process performance. Negative information is an indication of which factor does not lead to improvement.The aim of this experiment is to analyze the effect of cutting parameters on the turned CSN 12050 carbon steel response of material removal rate (MRR).The experiment is planned using Taguchi’s orthogonal array in design of experiments (DOEs) which help in reducing the number of experiments. The experiments are conducted according to a three level L9(34) orthogonal arrays (OAs). In this research to conduct the experiment dry turning process of CSN 12050 carbon steel is performed on CNC lathe.Orthogonal arrays (OAs) were mathematical invention recorded in early 1897 by Jacques Hadamard, a French mathematician. The Hadamard matrices are identical mathematically to the Taguchi matrices; the columns and rows are rearranged [13].Nomenclatures of arrays can be defined [13, 15] as La(bc) where L is Latin square, a is number of rows, b is number of levels, and c is number of columns (factors).Degrees of freedom (DOF) are associated with theOAs = a- 1.The experimental design considered for the investigation to achieve an optimal material removal rate during the turning process of CSN 12050 carbon steel is based on theL9; orthogonal arrays are shown in Table 1. Orthogonal arrays are a special standard experimental design that requires only a small number of experimental trials to find the main control factor effects on the output or response of the turning process [15]. After conducting a number of trials on the required cutting parameters the appropriate ranges of design matrix of variables to theOAs are set as given in Table 2.Table 1
Selected orthogonal array and levels.
Experiment Number Process control factors A B C 1 1 1 1  2 1 2 2  3 1 3 3  4 2 1 2  5 2 2 3  6 2 3 1  7 3 1 3  8 3 2 1  9 3 3 2Table 2
Design matrix of variables to the orthogonal array.
Experiment Number Cutting Speed Depth of Cut Feed Rate V c , ( m / m i n ) C d , ( m m ) f r , ( m m / r e v ) 1 100 0.25 0.10  2 100 0.35 0.20  3 100 0.50 0.25  4 175 0.25 0.20  5 175 0.35 0.25  6 175 0.50 0.10  7 275 0.25 0.25  8 275 0.35 0.10  9 275 0.50 0.20


### 2.2. Experimental Setup

Tables3 and 4 indicated process parameters used for the current work such the control parameters, the levels used in the experiment, experimental setup, and conditions. Analysis of variance forS/Nratio was evaluated by using Minitab 17 software. The cutting parameters were identified as cutting speed, Vc (m/min) depth of cut, Cd (mm), and feed rate, fr (mm/rev).Table 3
Selected levels for the factors.
Control Parameters Units Codes as per Minitab Levels 1 2 3 Cutting speed(Vc) m/min A 100 175 275  Depth of cut(Cd) mm B 0.25 0.35 0.50  Feed rate(fr) mm/rev C 0.10 0.20 0.25Table 4
Experimental setup and machining conditions.
Workpiece materials CSN 12050 carbon steel  Size (diameter x length) 37 x 100 mm  Cutting tool inserts Sandvik Coromant carbide insert  Machine tool CNC center lathe  Cutting condition Dry machiningFigure2 shows the experimental setup on CNC center lathe of a model TOS-TRENCIN sn40 UPGRADED GSK CNC 980TDb with Power voltage 380V; Control voltage 110 V; Illuminating lamp voltage 24V; Total CAP 2KVA; Frequency 50; Phase 3; Degree of protection IP54; Fusing current 40 A; Swing Over Bed ways: 16″ and Distance Between Centers: 60″ for the turning operation. A round bar specimen of CSN 12050 carbon steel with a diameter of 37mm and 100 mm length was mounted. The cutting tool used for this experimentation is the Sandvik Coromant carbide insert tool with dimensions of 20 x 20 x 200 mm (w×h×l in mm) shown in Figure 3.Figure 2
Experimental setup on CNC center lathe (TOS-TRENCIN sn40 UPGRADED GSK CNC 980TDb) for the turning operation of specimen of CSN 12050 carbon steel.Figure 3
Tool of Sandvik Coromant carbide insert cutter with dimensions of 20 x 20 x 200 mm (w×h×l in mm).


## 2.1. Design of Experiments

“Experimental Design (ED) is a strategy of planning, conducting, analyzing, and interpreting experiments so that sound and valid conclusions can be drawn efficiently, effectively, and economically” [14, 15]. Design of Experiment (DOE) with Taguchi is divided into three main phases which are planning, conducting, and analysis phase [15]. The planning phase is the most important phase for experiment to provide the expected information. Sometimes the information from the experiment is in positive or negative sense. Positive information is an indication of which factor and which level lead to improving product or process performance. Negative information is an indication of which factor does not lead to improvement.The aim of this experiment is to analyze the effect of cutting parameters on the turned CSN 12050 carbon steel response of material removal rate (MRR).The experiment is planned using Taguchi’s orthogonal array in design of experiments (DOEs) which help in reducing the number of experiments. The experiments are conducted according to a three level L9(34) orthogonal arrays (OAs). In this research to conduct the experiment dry turning process of CSN 12050 carbon steel is performed on CNC lathe.Orthogonal arrays (OAs) were mathematical invention recorded in early 1897 by Jacques Hadamard, a French mathematician. The Hadamard matrices are identical mathematically to the Taguchi matrices; the columns and rows are rearranged [13].Nomenclatures of arrays can be defined [13, 15] as La(bc) where L is Latin square, a is number of rows, b is number of levels, and c is number of columns (factors).Degrees of freedom (DOF) are associated with theOAs = a- 1.The experimental design considered for the investigation to achieve an optimal material removal rate during the turning process of CSN 12050 carbon steel is based on theL9; orthogonal arrays are shown in Table 1. Orthogonal arrays are a special standard experimental design that requires only a small number of experimental trials to find the main control factor effects on the output or response of the turning process [15]. After conducting a number of trials on the required cutting parameters the appropriate ranges of design matrix of variables to theOAs are set as given in Table 2.Table 1
Selected orthogonal array and levels.
Experiment Number Process control factors A B C 1 1 1 1  2 1 2 2  3 1 3 3  4 2 1 2  5 2 2 3  6 2 3 1  7 3 1 3  8 3 2 1  9 3 3 2Table 2
Design matrix of variables to the orthogonal array.
Experiment Number Cutting Speed Depth of Cut Feed Rate V c , ( m / m i n ) C d , ( m m ) f r , ( m m / r e v ) 1 100 0.25 0.10  2 100 0.35 0.20  3 100 0.50 0.25  4 175 0.25 0.20  5 175 0.35 0.25  6 175 0.50 0.10  7 275 0.25 0.25  8 275 0.35 0.10  9 275 0.50 0.20


## 2.2. Experimental Setup

Tables3 and 4 indicated process parameters used for the current work such the control parameters, the levels used in the experiment, experimental setup, and conditions. Analysis of variance forS/Nratio was evaluated by using Minitab 17 software. The cutting parameters were identified as cutting speed, Vc (m/min) depth of cut, Cd (mm), and feed rate, fr (mm/rev).Table 3
Selected levels for the factors.
Control Parameters Units Codes as per Minitab Levels 1 2 3 Cutting speed(Vc) m/min A 100 175 275  Depth of cut(Cd) mm B 0.25 0.35 0.50  Feed rate(fr) mm/rev C 0.10 0.20 0.25Table 4
Experimental setup and machining conditions.
Workpiece materials CSN 12050 carbon steel  Size (diameter x length) 37 x 100 mm  Cutting tool inserts Sandvik Coromant carbide insert  Machine tool CNC center lathe  Cutting condition Dry machiningFigure2 shows the experimental setup on CNC center lathe of a model TOS-TRENCIN sn40 UPGRADED GSK CNC 980TDb with Power voltage 380V; Control voltage 110 V; Illuminating lamp voltage 24V; Total CAP 2KVA; Frequency 50; Phase 3; Degree of protection IP54; Fusing current 40 A; Swing Over Bed ways: 16″ and Distance Between Centers: 60″ for the turning operation. A round bar specimen of CSN 12050 carbon steel with a diameter of 37mm and 100 mm length was mounted. The cutting tool used for this experimentation is the Sandvik Coromant carbide insert tool with dimensions of 20 x 20 x 200 mm (w×h×l in mm) shown in Figure 3.Figure 2
Experimental setup on CNC center lathe (TOS-TRENCIN sn40 UPGRADED GSK CNC 980TDb) for the turning operation of specimen of CSN 12050 carbon steel.Figure 3
Tool of Sandvik Coromant carbide insert cutter with dimensions of 20 x 20 x 200 mm (w×h×l in mm).


## 3. Results and Discussion

In order to analyze the value ofMRRby combining with the result of experimentation the amount of MRR obtained by turning using CNC lathe can be computed by substituting (2), (3), (4), and (5) into (7), we can develop the new equation (8) as [9] (8)MRR=ρsπr2hi-ρsπr2hfρstcf-tcimm3/minwhere ρs is density of CSN 12050 carbon steel = (7.85 x 10-3g/mm3); h, is height of the machined workpiece material; r is radius of workpiece material; tcf is final cycle time; tci is initial cycle time; i is initial, and f is final.Turning operation on a CNC center lathe using carbide tool has made on CSN 12050 carbon steel of 37 mm diameter and 100 mm length and conducted for 3 reputations. The influence of cutting speed (Vc), depth of cut (Cd), and feed rate (fr) on theMRR is observed as per predesigned L9, orthogonal array. The experimental results ofMRR are tabulated in Table 5.Table 5
TaguchiL9 orthogonal array experimental design matrix results of performance parameters of material removal rate with the corresponding control factors.
TrialNumber Cutting SpeedVc,(m/min) Depth of cutCd,(mm) Feed ratefr,(mm/rev) MaterialRemoval RateMRR,(mm3/min) S/N Ratio (dB) 01 100 0.25 0.10 1484.45 63.43  02 100 0.35 0.20 3973.00 71.98  03 100 0.50 0.25 3694.66 71.35  04 175 0.25 0.20 2906.25 69.27  05 175 0.35 0.25 4477.32 73.02  06 175 0.50 0.10 2687.79 68.59  07 275 0.25 0.25 5008.00 73.99  08 275 0.35 0,10 4186.80 72.44  09 275 0.50 0.20 3829.74 71.66  The total mean S/N ratios value 70.64


### 3.1. Effect of Control Factors on Material Removal Rate

To achieve high quality and low-cost products, the analysis of process parameters onMRR are very significant for any material before real time applications. In this concern, the experiments are performed on CSN 12050 carbon steel under variable control factors to predict the optimized material removal rate (MRR) during turning process. As per the previous discussion,L9 orthogonal array is used to proceed the experiments and the experimental results shown in Table 5 and analyzed using Taguchi method.TheS/N ratio results onMRR are calculated using the larger-the better criterion. Hence, the meanS/N ratio for each level of the factors cutting speed (m/min), depth of cut (mm), and feed rate is summarized in Table 5. The calculated total meanS/N ratio has been illustrated in Table 6. The response table indicates the average of the selected characteristics for each level of factors and includes rank based on delta statistics, which compare the relative extent of effects. The delta statistic is calculated as the difference of the highest average and the lowest average for each factor. Thus, the ranks are assigned based on the delta values. The Minitab 17 software assigns rank 1st to the highest delta value for fr (mm/rev) rank 2nd to the second highest for Vc, (m/min) and 3rd to the Cd (mm). The main effects plots for each of the process parameters have been shown in Figure 4.Table 6
Response table of mean signal to noise ratios.
Level Cutting speedVc,(m/min) Depth of cutCd,(mm) Feed ratefr,(mm/rev) Code – A Code – B Code – C 1 68.92 68.90 68.15  2 70.29 72.48 70.97  3 72.70 70.53 72.79  Delta 3.78 3.58 4.64  Rank 2 3 1  The total mean S/N ratios value = 70.637Figure 4
The main effect plot forS/N ratios data means forMRR(mm3/min).The control parameterfr (mm/rev) and Vc (m/min) are the most significant influence on the optimization ofMRR(mm3/min) while the control parameter Cd (mm) has some shared effect and the combination ofS/N ratio is A3B2C3.MRRis the higher-the better type quality characteristics and can be seen from Figure 4, by which the third level (A3) of cutting speed = 275 (m/min), the third level of feed rate (C3) = 0.25 (mm/rev), and the second level of depth of cut (B2) = 0.35 (mm) provide maximum value ofMRR(mm3/min). It can be observed from Figure 4 thatMRR is initially increased with increase in Cd (mm) and then decreased with further increase in Cd (mm). TheMRRis increased continuously with increase in feed rate and cutting speed.The use of bothANOVA technique andS/N ratio approach makes it easy to analyze the results and make it fast to reach the optimum control parameters for better response ofMRRand to give flawless conclusion. ANOVA determines the ratio between the regression mean square and mean square error and is termed as F–ratio or variance ratio as shown in Table 7.Table 7
Analysis of variance (ANOVA) results for MRR(mm3/min).
Source DOF ADj (SS) Adj (MS) F – value P – Value Contribution %C A 2 2729112 1364556 1.29 0.342 18.51 %  B 2 1892157 946079 0.79 0.496 12.83 %  C 2 3874391 1937196 2.23 0.189 26.27 %  Error 6 6251873  Total 12 14747533Table7 shows the percentage contribution of each of the parameters. Accordingly, the same trend as the main effect plots can be observed for the given process control parameters such as the parameters (C), fr (mm/rev), and (A), Vc (m/min) had the most significant influence onMRRwhile the parameter (B), Cd (mm), was not significant within the specific experimental range.


### 3.2. Confirmation Tests

Confirmation test is required to determine the accuracy of the result obtained from the Minitab software to achieve the optimal level of processes parameters [9, 15]. Once the optimal level of the process parameters is selected, the final step is to predict and verify the improvement of the performance characteristic using the optimal level of the process parameters. The estimatedS/N ratio(η⌢), using the optimal level of the process parameters, can be calculated as given in [9](9)η⌢=ηm+∑i=1qηi¯-ηmwhereηm is the total mean of the S/N ratio, η-i is the mean S/N ratio at the optimal level, and q is the number of the process parameters that significantly affect the performance characteristic. In Table 7 result of the estimatedS/Nratio (73.927) and the actualS/N ratio (74.091) shows an insignificant difference. The parametric analysis had been carried out to study the influences of the input process parameter onMRR in turning process using CNC lathe. The contour and the response surface plots forMRR with respect to selected input turning parameters are presented in Figure 5. From the plots it is clear thatMRR(mm3/min) increases with an increase of feed rate and cutting speed. This increase becomes more noticeable as the value of feed and cutting speed rises, due to higher rate of material removal.Figure 5
3D surface and contour plots for turning process control factors versusMRR(mm3/min).
(a) (b) (c)After selecting the optimum level of cutting process parameters, the final step is checked and determines the improvement ofMRR(mm3/min). To predict and verify the optimization in performance characteristic by using the optimum level cutting parameters the experimentation was once again performed initially at A2B3C2 and the optimized values A3B2C3. The parametric combination for maximumMRR(mm3/min) was observed at A3B2C3, that is, cutting speed 275 (m/min), depth of cut 0.35 (mm), and feed rate 0.25 (mm/rev). Table 8 also confirms the reproducibility of experimental conclusion due to its insignificant variations of the results between the experimental and theoretical values.Table 8
Confirmation test results and comparison ofS/N ratios.
Measurement Indicators Level MRR S/N ratios (dB) Initial parameters A 2 B 3 C 3 2773.87 68.862  Optimal parameters Theoretical A 3 B 2 C 3  73.927 Experimental A 3 B 2 C 3 5065.15 74.091 Improvement of S/N ratio = 5.23 dB.The progress of theS/N ratio from the initial cutting parameters to the optimal cutting parameters is 5.23 (dB). Consequently, the material removal rate in turning operations is significantly improved through the implemented (DOE) approach.From Figure6 one can easily understand at a glance, as it is a common interest response of Mean Signal-to-Noise Ratios Larger is better for optimization of material removal rate (mm3/min). As we can observe from the plot, the effect of bonding force on the pull strength is different at low and high levels of cutting speed. An interaction between factors occurs when the change in response from the low level to the high level of one factor is not the same. That is, the effect of one factor is dependent upon a second factor. It is observed that the interactions plots are used to compare the relative strength of the effects across control factors in turning process as given in Figure 6.Figure 6
Interaction plots forMRR(mm3/min) with the corresponding process control factors.The residual plots for material removal rate (mm3/min) are shown in Figure 7. These are generally fall on a straight line implying that errors are distributed normally. From the figure, it can be concluded that all values are within the control range, indicating that there is no an unusual structure and also the residual analysis does not indicate any model insufficiency. Hence, these values yield better results in future predictions.Figure 7
Residual plots forMRR(mm3/min) due to the effect of process control factors.


## 3.1. Effect of Control Factors on Material Removal Rate

To achieve high quality and low-cost products, the analysis of process parameters onMRR are very significant for any material before real time applications. In this concern, the experiments are performed on CSN 12050 carbon steel under variable control factors to predict the optimized material removal rate (MRR) during turning process. As per the previous discussion,L9 orthogonal array is used to proceed the experiments and the experimental results shown in Table 5 and analyzed using Taguchi method.TheS/N ratio results onMRR are calculated using the larger-the better criterion. Hence, the meanS/N ratio for each level of the factors cutting speed (m/min), depth of cut (mm), and feed rate is summarized in Table 5. The calculated total meanS/N ratio has been illustrated in Table 6. The response table indicates the average of the selected characteristics for each level of factors and includes rank based on delta statistics, which compare the relative extent of effects. The delta statistic is calculated as the difference of the highest average and the lowest average for each factor. Thus, the ranks are assigned based on the delta values. The Minitab 17 software assigns rank 1st to the highest delta value for fr (mm/rev) rank 2nd to the second highest for Vc, (m/min) and 3rd to the Cd (mm). The main effects plots for each of the process parameters have been shown in Figure 4.Table 6
Response table of mean signal to noise ratios.
Level Cutting speedVc,(m/min) Depth of cutCd,(mm) Feed ratefr,(mm/rev) Code – A Code – B Code – C 1 68.92 68.90 68.15  2 70.29 72.48 70.97  3 72.70 70.53 72.79  Delta 3.78 3.58 4.64  Rank 2 3 1  The total mean S/N ratios value = 70.637Figure 4
The main effect plot forS/N ratios data means forMRR(mm3/min).The control parameterfr (mm/rev) and Vc (m/min) are the most significant influence on the optimization ofMRR(mm3/min) while the control parameter Cd (mm) has some shared effect and the combination ofS/N ratio is A3B2C3.MRRis the higher-the better type quality characteristics and can be seen from Figure 4, by which the third level (A3) of cutting speed = 275 (m/min), the third level of feed rate (C3) = 0.25 (mm/rev), and the second level of depth of cut (B2) = 0.35 (mm) provide maximum value ofMRR(mm3/min). It can be observed from Figure 4 thatMRR is initially increased with increase in Cd (mm) and then decreased with further increase in Cd (mm). TheMRRis increased continuously with increase in feed rate and cutting speed.The use of bothANOVA technique andS/N ratio approach makes it easy to analyze the results and make it fast to reach the optimum control parameters for better response ofMRRand to give flawless conclusion. ANOVA determines the ratio between the regression mean square and mean square error and is termed as F–ratio or variance ratio as shown in Table 7.Table 7
Analysis of variance (ANOVA) results for MRR(mm3/min).
Source DOF ADj (SS) Adj (MS) F – value P – Value Contribution %C A 2 2729112 1364556 1.29 0.342 18.51 %  B 2 1892157 946079 0.79 0.496 12.83 %  C 2 3874391 1937196 2.23 0.189 26.27 %  Error 6 6251873  Total 12 14747533Table7 shows the percentage contribution of each of the parameters. Accordingly, the same trend as the main effect plots can be observed for the given process control parameters such as the parameters (C), fr (mm/rev), and (A), Vc (m/min) had the most significant influence onMRRwhile the parameter (B), Cd (mm), was not significant within the specific experimental range.


## 3.2. Confirmation Tests

Confirmation test is required to determine the accuracy of the result obtained from the Minitab software to achieve the optimal level of processes parameters [9, 15]. Once the optimal level of the process parameters is selected, the final step is to predict and verify the improvement of the performance characteristic using the optimal level of the process parameters. The estimatedS/N ratio(η⌢), using the optimal level of the process parameters, can be calculated as given in [9](9)η⌢=ηm+∑i=1qηi¯-ηmwhereηm is the total mean of the S/N ratio, η-i is the mean S/N ratio at the optimal level, and q is the number of the process parameters that significantly affect the performance characteristic. In Table 7 result of the estimatedS/Nratio (73.927) and the actualS/N ratio (74.091) shows an insignificant difference. The parametric analysis had been carried out to study the influences of the input process parameter onMRR in turning process using CNC lathe. The contour and the response surface plots forMRR with respect to selected input turning parameters are presented in Figure 5. From the plots it is clear thatMRR(mm3/min) increases with an increase of feed rate and cutting speed. This increase becomes more noticeable as the value of feed and cutting speed rises, due to higher rate of material removal.Figure 5
3D surface and contour plots for turning process control factors versusMRR(mm3/min).
(a) (b) (c)After selecting the optimum level of cutting process parameters, the final step is checked and determines the improvement ofMRR(mm3/min). To predict and verify the optimization in performance characteristic by using the optimum level cutting parameters the experimentation was once again performed initially at A2B3C2 and the optimized values A3B2C3. The parametric combination for maximumMRR(mm3/min) was observed at A3B2C3, that is, cutting speed 275 (m/min), depth of cut 0.35 (mm), and feed rate 0.25 (mm/rev). Table 8 also confirms the reproducibility of experimental conclusion due to its insignificant variations of the results between the experimental and theoretical values.Table 8
Confirmation test results and comparison ofS/N ratios.
Measurement Indicators Level MRR S/N ratios (dB) Initial parameters A 2 B 3 C 3 2773.87 68.862  Optimal parameters Theoretical A 3 B 2 C 3  73.927 Experimental A 3 B 2 C 3 5065.15 74.091 Improvement of S/N ratio = 5.23 dB.The progress of theS/N ratio from the initial cutting parameters to the optimal cutting parameters is 5.23 (dB). Consequently, the material removal rate in turning operations is significantly improved through the implemented (DOE) approach.From Figure6 one can easily understand at a glance, as it is a common interest response of Mean Signal-to-Noise Ratios Larger is better for optimization of material removal rate (mm3/min). As we can observe from the plot, the effect of bonding force on the pull strength is different at low and high levels of cutting speed. An interaction between factors occurs when the change in response from the low level to the high level of one factor is not the same. That is, the effect of one factor is dependent upon a second factor. It is observed that the interactions plots are used to compare the relative strength of the effects across control factors in turning process as given in Figure 6.Figure 6
Interaction plots forMRR(mm3/min) with the corresponding process control factors.The residual plots for material removal rate (mm3/min) are shown in Figure 7. These are generally fall on a straight line implying that errors are distributed normally. From the figure, it can be concluded that all values are within the control range, indicating that there is no an unusual structure and also the residual analysis does not indicate any model insufficiency. Hence, these values yield better results in future predictions.Figure 7
Residual plots forMRR(mm3/min) due to the effect of process control factors.


## 4. Conclusions

In the present work, a study has been carried out to optimize the process control parameters: cutting speed, depth of cut, and feed rate with respect toMRR in dry turning environment using CNC lathe. The material used for the investigation was CSN 12050 carbon steel. The L9,(34) orthogonal array has been employed for experimental design and analysis ofMRR. The experimental results based onS/N ratio approach andANOVA analysis provides a systematic and efficient methodology for optimization of cutting parameters for maximizedMRR and ease of machinability. The results based onS/N ratios andANOVA show that feed rate has most significant factor compared to cutting speed and depth of cut has least significant factor. The optimal combination of cutting parameters forMRR is cutting speed 275 (m/min), depth of cut 0.35 (mm), and feed rate 0.25 (mm/rev).The final experiments for confirmation have been carried out to validate the optimal cutting parameters. The comparison from initial cutting parameters to the optimal cutting parameters about 5.229 (dB) improvement ofMRR is observed. Moreover, it is noticeable that machining at higher cutting speeds leads to shorter cycle times and maximized material removal rate. Hence, the results confirmed the substantial role of dry turning process to realize the ultimate goal of product sustainability optimization for economically, environmentally, and societally sound-full machining.



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*Source: 7150157-2019-04-01.xml*