PREDICTION OF OPTIMAL DESIGNS FOR MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS CHARACTERISTICS

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1 International Journal of Lean Thinking PREDICTION OF OPTIMAL DESIGNS FOR MATERIAL REMOVAL RATE AND SURFACE ROUGHNESS CHARACTERISTICS Maheswara Rao Ch a*, Venkatasubbaiah K b, Suresh Ch c a Department of Mechanical, Assistant Professor, Raghu Institute of Technology, Visakhapatnam , Andhra Pradesh, India b Department of Mechanical, Professor, Andhra University, Visakhapatnam , Andhra Pradesh, India c Department of Mechanical, Scholar, Andhra University, Visakhapatnam , Andhra Pradesh, India A B S T R A C T The present work involves in finding the optimal combination of cutting parameters, in dry turning of EN19 steel using a tungsten carbide tool of nose radius 0.4 mm. The experiments were conducted on a CNC turret lathe as per the designed L9 (3^3) orthogonal array. In order to optimize the Material Removal Rate (MRR), Arithmetic Average Roughness (Ra) and Average Peak-to-Valley Height Roughness (Rz) individually, Single objective Taguchi method has been employed. From the results, the optimal combination of cutting parameters for MRR is found at: 225 m/min, 0.15 mm/rev and 0.6 mm. Optimal combination of Ra and Rz is found at: 225 m/min, 0.05 mm/rev and 0.6 mm. Analysis of variance (ANOVA) is used to find the influence of cutting parameters on the responses. ANOVA results revealed that speed and feed has high influence on MRR. Speed has high influence in affecting the Roughness parameters. Linear regression models for the responses were prepared using the MINITAB-16 software. From the results, it is found that the models prepared are more significant and accurate. K E Y W O R D S EN19 steel; Surface roughness; Taguchi; Regression; ANOVA. A R T I C L E I N F O Received 18 Aug 2016 Accepted 25 Dec 2016 Available online 5 Jan 2017 * Corresponding author: Ch. Maheswara Rao mahe.mech129@gmail.com,tel.:

2 1. Introduction In manufacturing industries, the challenges that the engineers come across are to find the optimal cutting parameters for the desired output and to maximize the performance using the available resources. In metal cutting processes, the fundamental metal removal operation is turning. In turning, the major desired outputs are material removal rate, productivity, quality, machining time, tool life and machining cost, etc. (H yanda et al. (2010); H K Vijaya Kumar et al. (2014)) C Ibrahim, 2006 conducted experiments to find the effect of cutting parameters on surface roughness in the machining of AISI304 and AISI316 steels using CVD multi-layer coated cemented carbide tools. The results showed that the cutting speed has high significance on surface roughness. G Shivam et al. (2016) conducted experiments on CNC lathe by taking cutting speed, feed and depth of cut as process parameters and MRR and SR as outputs. The Cutting speed and depth of cut are the most influencing parameters on surface roughness and material removal rate respectively. In the present work, an experiment has been made to find the optimal combination of cutting parameters and their influence on Material Removal Rate (MRR) and Surface Roughness (R a and R z ) characteristics. There are many factors which influence the responses. (R Gupta and A Diwedi, 2014; K Pavan Kumar Reddy et al. (2014)) Surface roughness is referred as the deviations measured from the mean value. It depends on many factors like machining parameters (Cutting speed, feed rate, depth of cut, cutting fluid), cutting tool properties (Tool material, tool shape, nose radius, rake angle, cutting edge, side cutting edge angle), work piece properties (hardness, length and diameter) and cutting phenomenon (Cutting force vibrations, chip formations, friction in the cutting zone), etc. (T Rama Krishna et al. (2014)) S Devendra et al. (2016) conducted a study to investigate the effect of nose radius on surface roughness in CNC turning of Aluminium 6061 in dry condition. Nose radius is identified as the most influencing parameter on surface roughness. S R Bheem et al. (2015) investigated the effect of cutting parameters on surface roughness and material removal rate during the turning of metal matrix composite. Results revealed that the feed is the most influencing parameter on surface roughness followed by the depth of cut and speed. It is customary to optimize all the influencing parameters in order to achieve a high material removal rate and good surface finish. Surface finish plays an important role in the evaluation of the quality of the product. Surface roughness affects the major functional attributes like contact causing surface friction, wear, light reflection, heat transmission, ability of distributing and holding a lubricant, coating and fatigue resistance, etc. (D Selvaraj and P Chandramohan, 2010) F Vishal, 2013 employed Taguchi method and Analysis of variance to find out the influence of cutting parameters on material removal rate and surface roughness. Feed rate is found to be the most influencing parameter on the surface roughness. Y Sahijpaul and S Gurpreet, 2013 analyzed the effect of cutting parameters on surface roughness while turning of EN8 steel. They concluded that the feed rate is the most influencing parameter on the surface roughness. The Surface roughness can be measured in terms of various parameters like R a, R q, R z, R t, R v, R p, R pm, S k and K, etc. Where, R a : Arithmetic Average (AA) or Centre Line Average (CLA), R q : Root Mean Square (RMS), R z : Average Peak-to-Valley height, R t : Extreme value height descriptor (R y, R max ) or Maximum Peakto-Valley height, R v : Maximum valley depth (or) Mean to Lowest Valley height, R p : Maximum Peak height (or) Maximum Peak to Mean height, R pm : Average Peak to mean height, S k : Skewness and K: Kurtosis, etc. Among all the roughness parameters, R a and R z are most significant from contact stiffness and surface wear point of view. (Ch Maheswara Rao and K Venkatasubbaiah, 2016a; Ch Maheswara Rao and K Venkatasubbaiah, 2016c) Hence, in this work Arithmetic Average (R a ) and Average Peakto-Valley height (R z ) are considered as the major experimental responses along with the material removal rate (MRR). For the present work, medium carbon steel EN19 is considered as work piece. EN19 steel is significant and most commonly used material because of its exclusive properties like high tensile strength, good 25

3 ductility and resistance to wear and shocks, etc. (E Edwin paul et al. (2013); SJ Raykar et al. (2014); A Kumar Roy and K Kumar, 2013) N Sateesh Kumar et al. (2012) investigates the effect of process parameters in turning of carbon alloy steels like SAE8620, EN8, EN19, EN24 and EN47 on a CNC lathe. The results reveal that the surface roughness increases with increased feed rate and is higher at lower speeds. H K Dave et al. (2012) conducted an experimental investigation of the machining characteristics of the different grades of EN materials in CNC turning process using TiN coated cutting tools. From the ANOVA results it is found that the depth of cut has high significance in producing high material removal rate and insert has high significance in producing lower surface roughness. In the present work the experiments are conducted as per the standard Taguchi s L9 orthogonal array at three different levels of cutting parameters. The optimum combination of cutting parameters for achieving a high Material removal rate and low surface roughness (R a and R z ) values was found, using single objective Taguchi method. (B M Gopalaswamy et al. (2009); S Thamizhmanji et al. (2007); H Kumar et al. (2013); I Asilturk et al. (2011); H Singh and P Kumar, 2006; J Varma et al. (2012)) Analysis of variance (ANOVA) has been employed to find the significance of the cutting parameters on the individual responses. (A Bhattacharya et al. (2009); M Kaladhar et al. (2012); A Kumar et al. (2012)) Ch Maheswara Rao et al. (2016) has investigated the effect of WEDM process parameters on the Surface roughness in machining of EN41 steel using Taguchi and ANOVA. They found the wire feed rate is the most influencing parameter on surface roughness. M S Ranganath et al. (2015) investigated the effect of cutting speed, feed rate and depth of cut on surface roughness in CNC turning of AA6061 in dry conditions. The speed was identified as the most influencing process parameter on the surface roughness. B Ravindra ramesh et al. (2016) studied the effect of spindle speed, depth of cut, feed rate, tool material and coolant flow on the surface roughness. Feed rate was found to be the dominantly affecting the surface roughness. Regression models for the responses have been developed using the MINITAB-16 software. The significance and the adequacy of the models were checked with their correlation coefficient (R 2 ) values and the Residual plots. (J Gokulachandran and K Mohandas, 2012; K D Dipti et al. (2014)) S Ashok Kumar et al. (2015) developed a prediction model for surface roughness in dry turning of AISI 1040 steel using a coated carbide insert. They used response surface methodology and artificial neural network methods were used for the prediction of the roughness model. Dabnun et al. (2005) developed model for surface roughness using factorial DOE and response surface methodology during machining of glass ceramic using uncoated carbide inserts under dry cutting conditions. From the results they identified, feed rate was the main influencing factor on the roughness, followed by the cutting speed and depth of cut. Finally, the experimental and predicted values from the models were compared to analyze the accuracy and the effectiveness of the models prepared. 2. Experimental Details Round bars of a medium carbon steel EN19 having dimensions, 25 mm diameter and 75 mm length were used as test specimens. EN materials have high applications in tool, oil and gas industries. They commonly used for axial shafts, propeller shafts, crank shafts, high tensile bolts and studs, connecting rods, riffle barrels and in gear manufacturing, etc. The chemical and mechanical properties of EN19 material are given in the tables 1 and 2 respectively. 26

4 Table 1. Chemical composition of EN19 Elements Composition (% weight) C Si Mn Cr Mo S max P max Ni - Table 2. Mechanical properties of EN19 Property Value Units Density 7.7 g/cm 3 Tensile Stress N/mm 2 Yield Stress 700 N/mm 2 Elongation 9 % Izod (Impact) 55 Joules Hardness BHN 2.1. Cutting tool material Tungsten carbide insert having ISO designation DNMG (Tool holder ISO designation PDJNL2525M16) was used for the experiment. The tungsten carbide tools have significant mechanical and thermal properties like high hardness, high elastic modulus, high temperature resistance and very low thermal expansion, etc. Because of sharp cutting edge, the tungsten carbide tools are significantly used for achieving high production rates and good surface finish. Tungsten carbide tools also used for attaining high cutting speed operations as they have high temperature resistance than other standard high speed steel tools. The cutting tool inserted in a tool holder was shown in the figure 1. Figure 1. Cutting tool and tool holder 2.2. Design of experiments (DOE) The experiments were conducted on CNC lathe (DX 200, Jobber XL, 20 KW, rpm) using a tungsten carbide tool under dry environment. For conducting the experiments Taguchi s standard L9 (3 levels * 3 factors) Orthogonal Array has been selected. The Taguchi s orthogonal array design used to study the entire parametric space with a small number of experiments hence, to reduce the overall 27

5 experimentation cost and time. L9 OA has 3 columns and 9 rows. The nine rows are assigned to nine test runs and three columns are assigned to cutting conditions of speed, feed and depth of cut respectively. The selected cutting parameters with their levels and the L9 OA with actual experimental values were given in the tables 3 and 4. Table 3. Selected cutting parameters with their levels Process Levels Notation Units parameters I II III Speed s m/min Feed f mm/rev Depth of cut d mm Table 4. L9 OA design with actual experimental values S.No. Design Speed Feed Depth of cut Design Design (m/min) (mm/rev) (mm) Measurement of material removal rate and surface roughness In the present work, the Material removal rate (MRR) and Surface roughness characteristics (R a and R z ) were considered as experimental responses. For the machined components, the material removal rate (MRR) is measured by multiplying the cutting parameters as given in the equation 1. The surface roughness was measured at three different points using SJ-301 (Mitutoyo) gauge shown in the figure 2 and the average is taken as the final value. The Roughness characteristics; R a (Arithmetic average) and R z (Average peak-to-valley height) can be measured mathematically using equations 2 and 3. The experimental test conditions are given in the table 5. MRR = v*f*d in cm 3 /min (1) Where, v is cutting speed in m/sec; f is feed rate in mm/rev and d is the depth of cut in mm. Ra = 1 n n i=1 Y i (2) Where, n is the total number of deviations, Y i is deviation from the mean line. 5 Rz = 1 [ R 5 i=1 pi R vi ] (3) Where, R pi and R vi are the I th highest peak and lowest valleys respectively. 28

6 Figure 2. Surface roughness tester Table 5. Experimental conditions Material Machine Cutting tool Cutting parameters and levels Orthogonal design Output characteristics Surface roughness tester EN19, Medium carbon steel Quantity: 9 no s Dimensions: 100 mm length and 25 mm diameter CNC turret lathe (DX-200, JOBBER XL, SEIMENS make) Input power: 20KW Spindle speed: 4000 RPM Tungsten carbide Insert: ISO DNMG Tool holder: ISO PDJNL2525M16 Speed: 75, 150 and 225 m/min Feed: 0.05, 0.1 and 0.15 mm/rev Depth of cut: 0.2, 0.4 and 0.6 mm L9 OA Material removal rate (MRR), Arithmetic average roughness (R a ) and Average peak-tovalley height roughness (R z ) SJ-301, Mitutoyo 29

7 3. Results and Discussions In Taguchi method the experimental results are transformed into Signal-to-Noise ratios to measure the quality characteristic deviations the desired value. Taguchi has suggested three types of Signal-to-Noise (S/N) ratios, they are Larger-the-Better, Smaller-the-Better and the Nominal-the-Better are represented in the equations 4, 5 and 6. In the present work, Larger-the-Better and Smaller-the-Better characteristics have been used for the analysis of Material Removal Rate and Surface Roughness characteristics (R a and R z ) respectively. The experimental results of Material removal rate and Surface roughness characteristics (R a and R z ) and the corresponding Signal-to-Noise (S/N) ratio values were given in the tables 6 and 7. Larger-the-Better (S/N): 10 log 10 [ 1 y i 2] (4) Where, y i is response value. Smaller-the-Better (S/N): 10 log 10 [y 2 i ] (5) Where, yi is response value. Nominal-the-Better (S/N): 10 log 10 [ μ2 2] (6) Where, µ is mean and σ is the variance. σ Table 6. Experimental results of MRR, R a and R z S.No. MRR R a R z (cm 3 /min) (µm) (µm) Table 7. Signal-to-Noise (S/N) ratios for MRR, R a and R z S.No. MRR R a R z

8 3.1. Taguchi results Parametric optimization of responses The experimental results are analyzed using the single objective Taguchi method. The method is based on the Signal-to-Noise ratios, the term signal and noise representing the desirable and undesirable experimental conditions. The calculated mean S/N ratios for the Material Removal Rate (MRR), Arithmetic average (R a ) and Average peak-to-valley height (R z ) were given in the tables 8, 9 and 10 respectively. Table 8. Response table for means of MRR Level s f d Delta Rank Table 9. Response table for means of R a Level s f d Delta Rank Table 10. Response table for means of R z Level s f d Delta Rank Main effect plots results Main effect plots are drawn for the Signal-to-Noise ratios of Material Removal Rate (MRR) and Surface Roughness characteristics (R a and R z ) using a statistical software MINITAB-16 version. In the main effect plot, the centre line represents the mean S/N ratio value. The cutting parameters with their levels and the S/N ratios of the responses are represented on X and Y-axes respectively. 31

9 Main Effects Plot for SN ratios Data Means 18 s f 15 Mean of SN ratios d Signal-to-noise: Larger is better 0.6 Figure 3. Main effect plot for S/N ratios of MRR Figure 3 shows, the variation of S/N ratios of Material Removal Rate with the increase in the levels of speed, feed rate and depth of cut. From the figure, we can observe a gradual increase in the S/N ratio with an increase in the levels of cutting parameters. The optimal combination of cutting parameters for the Material removal rate was found at levels corresponding to highest S/N ratios i.e. at s3-f3-d3. Figure 4 shows, the effect of cutting parameters on the Arithmetic average roughness (R a ). From the figure, it is observed that the R a is mainly affected due to cutting speed and followed by feed and depth of cut. The high S/N ratio of surface roughness (R a ) value is observed at high cutting speed, i.e. at v3, i.e. R a is optimal at 225 m/min. Similarly, the high S/N ratio of R a was observed at the lower feed rate, i.e. at f1 (0.05 mm/rev). It is because of the surface roughness value increases with an increase in the feed rate as R a = f2 32r ; where f is feed rate in mm/rev and r is nose radius of the tool in mm. The optimal combination of cutting parameters for R a was obtained at v3-f1-d3. 32

10 Main Effects Plot for SN ratios Data Means s f -4-6 Mean of SN ratios d Signal-to-noise: Smaller is better 0.6 Figure 4. Main effect plot for S/N ratios of R a Main Effects Plot for SN ratios Data Means -16 s f -18 Mean of SN ratios d Signal-to-noise: Smaller is better 0.6 Figure 5. Main effect plot for S/N ratios of R z 33

11 Figure 5 shows, the change in the S/N ratios of Average peak-to-valley height (R z ) with an increase in the levels of cutting parameters. The optimal combination of cutting parameters for R z is obtained at v3- f1-d3. Table 11. Optimal combination of cutting parameters Cutting parameter Speed(s), (m/min) Feed (f), (mm/rev) Depth of cut (d), (mm) MRR (cm 3 /min) R a (µm) R z (µm) Level Value Level Value Level Value ANOVA results The influence of cutting parameters on the responses was analyzed using Analysis of variance. The ANOVA is conducted at a confidence level of 95% i.e. at α = The results of ANOVA for Material Removal Rate (MRR), Arithmetic Average roughness (R a ) and Average peak-to-valley height roughness (R z ) are given in the tables 12, 13 and 14 respectively. From the results, it is clear that for MRR cutting speed and feed rate (F = 1.9) are the high influencing parameters. Similarly, for R a and R z cutting speed (F = 247, 54.46) is the most influencing parameter and followed by feed rate (F = , 27.05) and depth of cut (F = 0.14, 1.9). Table 12. ANOVA results of MRR Source DOF Seq SS Adj SS Adj MS F P s f d Error Total Table 13. ANOVA results of R a Source DOF Seq SS Adj SS Adj MS F P s f d Error Total Table 14. ANOVA results of R z Source DOF Seq SS Adj SS Adj MS F P s f d Error Total

12 3.4. Prediction of optimal design for MRR, R a and R z The optimal design of responses can be predicted by considering most significant factors at their best levels. Optimal design of MRR For the Material Removal Rate (MRR), speed and feed are the most significant factors at v3 and f3 levels. µa3b3 = A3 + B3 T Where, A3 = 8.25, B3 = 8.25 (From mean table 8) T = 5.75 µa3b3 = A3 + B3 T = = CI = (F 95%,1,doferrorV error ) (n eff ) N Where, ƞ eff = (1+dof) ; where, N is total number of experiments, dof is degree of freedom. ƞeff = 9 (1+2+2) = 1.8 Verror = 9 (From ANOVA table 12) F95%,1,2 = (From standard F-table at α = 0.05) CI = ( ) 1.8 = The predicted optimal range = µa3b3 CI µa3b3 µa3b3 + CI = µa3b = µa3b Optimal design of R a The most significant parameters corresponding to R a are speed and feed at their v3 and f1levels. µa3b3 = A3 + B1 T Where, A3 = 1.533, B1 = (From mean table 9) T = µa3b3 = A3 + B1 T = = CI = (F 95%,1,doferrorV error ) (n eff ) N Where, ƞ eff = (1+dof) ; where, N is total number of experiments, dof is degree of freedom. 35

13 ƞeff = 9 (1+2+2) = 1.8 Verror = (From ANOVA table 13) F95%,1,2 = (From standard F-table at α = 0.05) CI = ( ) 1.8 = The predicted optimal range = µa3b1 CI µa3b1 µa3b1 + CI = µa3b = µa3b Optimal design of R z The most significant parameters corresponding to Rz are speed and feed at their v3 and f1levels. µa3b1 = A3 + B1 T Where, A3 = 7.133, B1 = 7.70 (From mean table 10) T = µa3b1 = A3 + B1 T = = CI = (F 95%,1,doferrorV error ) (n eff ) Where, ƞeff = ƞeff = 9 N (1+dof) (1+2+2) = 1.8 Verror = (From ANOVA table 14) ; where, N is total number of experiments, dof is degree of freedom. F95%,1,2 = (From standard F-table at α = 0.05) CI = ( ) 1.8 = The predicted optimal range = µa3b1 CI µa3b1 µa3b1 + CI = µa3b = µa3b Regression results The relation between the output characteristics and cutting parameters can be obtained using linear regression analysis. In the present work, mathematical models of the responses were prepared by MINITAB-16 statistical software. First order polynomial model y = b 0 + b 1 x 1 + b 2 x b n x n (7) 36

14 Where, b0, b1, b2 are regression coefficients. x, y are independent and dependent variables. Second order polynomial model i=k i j=k ii=k i=1 i j=1 ii=1 (8) y = b 0 + b i x i + b ij x i x j + b ii x ii 2 Where, i, j are indexes, k is number of factors, b 0, b i, b ij, b ii are regression coefficients and x and y are independent and dependent variables. The regression equation for MRR is MRR = s f d (9) Table 15. Regression analysis for MRR Predictor coef SE coef T P Constant s f d R 2 = 81.8%, R 2 (Adj) = 70.9% Table 16. ANOVA for MRR Source DF SS MS F P Significance Regression significant Residual error Total Tables 15 and 16 shows, the results of Regression analysis and ANOVA for Material Removal Rate. ANOVA is used for identifying the level of significance of the model developed. From the regression analysis, the model prepared for MRR is more significant because of high coefficient determination (R 2 = 81.8%) value. The Normality and constant variance assumptions of ANOVA are verified with the Normal probability and versus fits plots shown in the figures 6 and 7. From the normal probability plot, it is clear that the errors are distributed normally as the residuals are lying nearer to the straight line. The model for MRR is adequate since the residuals do not represent any regular pattern in versus fits plot. 37

15 99 Normal Probability Plot (response is MRR) Percent Residual Figure 6. Normal probability plot for MRR Versus Fits (response is MRR) 3 2 Residual Fitted Value Figure 7. Versus fits plot for MRR 38

16 The regression analysis and ANOVA results of Arithmetic average roughness (R a ) are given in the tables 17 and 18. The coefficient of determination value (R 2 ) for the model R a is found to be 99.7%, i.e. it is close to 0.99 hence the model is more significant. The Normal probability and versus fits plots drawn for Ra are shown in the figures 8 and 9. The figures representing that the model is following normal distribution and it is more adequate. The regression equation for R a is R a = s f d (10) Table 17. Regression analysis for R a Predictor coef SE coef T P Constant s f d R 2 = 99.7%, R 2 (Adj) = 99.5% Table 18. ANOVA for R a Source DF SS MS F P Significance Regression significant Residual error Total Normal Probability Plot (response is Ra) Percent Residual Figure 8. Normal probability plot for R a 39

17 Versus Fits (response is Ra) Residual Fitted Value Figure 9. Versus fits plot for R a The regression analysis and ANOVA results of Average peak-to-valley height roughness (R z ) are given in the tables 19 and 20. The coefficient of determination (R 2 ) value for the model R z is found to be 96.9%, i.e. it is close to 1 hence the model is more significant. For the model R z the errors are normally distributed and the model is adequate which are found from the figures 10 and 11. The regression equation for R z is R z = s f 3.17 d (11) Table 19. Regression analysis for R z Predictor coef SE coef T P Constant s f d R 2 = 96.9%, R 2 (Adj) = 95.1% Table 20. ANOVA for R z Source DF SS MS F P Significance Regression significant Residual error Total

18 99 Normal Probability Plot (response is Rz) Percent Residual Figure 10. Normal probability plot for R z 1.0 Versus Fits (response is Rz) 0.5 Residual Fitted Value Figure 11. Versus fits plot for R z 41

19 MRR in cm3/min Maheswara Rao Ch., Venkatasubbaiah K., Suresh Ch. / International Journal of Lean Thinking 3.6. Comparison of experimental and predicted values The experimental results and the predicted values from the mathematical models of the responses were compared. The comparison plots drawn using the EXCEL by taking the experimental number on the X- axis and the response value are on Y-axis. From the figures 12, 13 and 14, it is found that both the experimental and predicted values are very close to each other hence, the models prepared for the responses (MRR, R a and R z ) are more accurate and they can be used for the best prediction of the responses Experimental Predicted Experiment Number Figure 12. Experimental (Vs.) predicted values of MRR 42

20 Rz in µm Ra in µm Maheswara Rao Ch., Venkatasubbaiah K., Suresh Ch. / International Journal of Lean Thinking 4 3,5 3 2,5 2 1,5 Experimental Predicted 1 0, Experiment Number Figure 13. Experimental (Vs.) predicted values of R a Experimental Predicted Experiment Number Figure 14. Experimental (Vs.) predicted values of R z 43

21 4. Conclusions From the experimental, Taguchi method, ANOVA and Regression analysis the following conclusions are made The optimal combination of cutting parameters for maximum Material Removal Rate (MRR) is obtained at: v3-f3-d3; 225 m/min, 0.15 mm/rev and 0.6 mm. The optimal combination of cutting parameters for minimum Arithmetic Average Roughness (R a ) and Average Peak-to-Valley height Roughness (R z ) are obtained at: v3-f1-d3; 225 m/min, 0.05 mm/rev and 0.6 mm. From the ANOVA results, the cutting speed and feed have high influence and depth of cut has low influence on Material Removal Rate (MRR). Cutting speed has high influence and followed by feed rate and depth of cut on Roughness parameters (R a and R z ). The optimal design for Material Removal Rate (MRR) is found in the range of to Similarly for Arithmetic Average Roughness (R a ) and Average Peak-to-Valley height Roughness (R z ) the optimal ranges are found in the range of to and to respectively. The Regression models prepared for the responses are more significant because of their high coefficient of determination (R 2 ) values and they are more adequate and accurate. References Ashok Kumar S., Arun Kumar R. and Dipti K.D. (2015). Response Surface and Artificial Neural Network Prediction Model and Optimization for Surface Roughness in Machinig. International Journal of Industrial Engineering Computations. 6; Asilturk I. and Akkus H. (2011). Determining the Effect of Cutting Parameters on Surface Roughness in Hard Turning Using Taguchi Method. Measurement. Vol. 44; Bhattacharya A., Das S., Majumdar P. and Ajay B. (2009). Estimation of the Effect of Cutting Parameters on Surface Finish and Power Consumption During High Speed Machining of AISI 1045 Steel Using Taguchi Design and ANOVA. Prod. Eng. Res. Devel.3, 31. Bheem S. R., Dharma Ram M. and Shrivastava S. (2015). Investigating the Effect of Cutting Parameters on Average Surface Roughness and Material Removal Rate During Turning of Metal Matrix Composite Using Response Surface Methodology. Vol.3, 1; Dabnun M.A., Hashmi M.S.J. and El-Baradie M.A. (2005). Surface Roughness Prediction Model by Design of Experiments for Turning Machinable Glass- Ceramic (Macor). Journal of Materials Processing Technology. 164; Dave H.K., Patel L.S. and Raval H. K. (2012). Effect of Machining Conditions on MRR and Surface Roughness During CNC Turning of Different Materials Using TiN Coated Cutting Tools by Taguchi Method. International Journal of Industrial Engineering Computations. Vol. 3; Dipti K. D., Ashok Kumar S., Ratnakar D. and Routara B.C. (2014). Investigations on Hard Turning Using Coated Carbide insert: Grey Based Taguchi and Regression Methodology. Elsvier Journal, Procedia Material Science 6; Devendra S., Vimanyu C. and Ranganadh M.S. (2016). Effect of Nose Radius on Surface Roughness During CNC Turning Using Response Surface Methodology. Vol.5, 2;

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