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20120140503011 2-3-4

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20120140503011 2-3-4

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 89 ANALYSIS OF TURNING PARAMETERS DURING TURNING OF AISI 1040 STEEL USING TAGUCHI METHOD Hassan Karim Mohammed1 , Dr. Mohammad Tariq2 , Dr. Fouad Alwan Saleh3 1 Ministry of Transport, General Maritime Transport Company, Republic of Iraq 2 Asst. Prof., Department of Mech. Engg., SSET, SHIATS-DU, Naini, Allahabad, U.P., India 3 Asst. Professor of Mechanical Engineering Department of Almustansiriya University, Iraq ABSTRACT The purpose of this research work is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning AISI 1040 alloy steel by Taguchi Method. Experiment was designed using Taguchi method and 27 experiments were designed by this process and experiments conducted. The results are analyzed using the average S/N ratios for smaller the better for surface roughness factors and significant interaction method. Taguchi method has shown that the depth of cut has significant role to play in producing lower surface roughness followed by feed. The Cutting speed has lesser role on surface roughness from the tests. The vibrations of the machine tool, tool chattering are the other factors which may contribute poor surface roughness to the results and such factors ignored for analyses. The results obtained by this method will be useful to other researches for similar type of study and may be better opportunity for further study on tool vibrations, cutting forces etc. Keywords: Taguchi method, Turning, AISI 1040, Surface Roughness. 1. INTRODUCTION Surface roughness influences the performance of mechanical parts and their production costs because it affects factors, such as friction, ease of holding lubricant, electrical and thermal conductivity, geometric tolerances and more. The ability of a manufacturing operation to produce a desired surface roughness depends on various parameters. The factors that influence surface roughness are machining parameters, tool and work piece material properties and cutting conditions. For example, in turning operation the surface roughness depends on cutting speed, feed rate, depth of cut, tool nose radius, lubrication of the cutting tool, machine vibrations, tool wear and on the mechanical and other properties of the material being machined. Even small changes in any of the mentioned factors may have a significant effect on the produced surface [1]. In machinability studies investigations, statistical design of experiments is used quite extensively. Statistical design of INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 3, March (2014), pp. 89-99 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 90 experiments refers to the process of planning the experiments so that the appropriate data can be analysed by statistical methods, resulting in valid and objective conclusions [2]. Design methods such as factorial designs, response surface methodology (RSM) and Taguchi methods are now widely use in place of one factor at a time experimental approach which is time consuming and exorbitant in cost. 1.1 Three Important Elements In order to get an efficient process and beautiful surface at the lathe machining, it is important to adjust a rotating speed, a cutting depth and a sending speed. Please note that the important elements cannot decide easily, because these suitable values are quiet different by materials, size and shapes of the part. They are Rotating Speed, Cutting Depth and Sending Speed (Feed). 1.2 Signal to Noise (S/N) Ratios The product/process/system design phase involves deciding the best values/levels for the control factors. The signal to noise (S/N) ratio is an ideal metric for that purpose. The equation for average quality loss, Q, says that the customer’s average quality loss depends on the deviation of the mean from the target and also on the variance. An important class of design optimization problem requires minimization of the variance while keeping the mean on target. 1.3 Taguchi method, design of experiment, and experimental details Taguchi defines the quality of a product, in terms of the loss imparted by the product to the society from the time the product is shipped to the customer [13]. Some of these losses are due to deviation of the product’s functional characteristic from its desired target value, and these are called losses due to functional variation. The uncontrollable factors which cause the functional characteristics of a product to deviate from their target values are called noise factors, which can be classified as external factors (e.g. temperatures and human errors), manufacturing imperfections (e.g. unit to unit variation in product parameters) and product deterioration. The overall aim of quality engineering is to make products that are robust with respect to all noise factors. Taguchi used the signal-to-noise (S/N) ratio as the quality characteristic of choice [1 and 13]. S/N ratio is used as a measurable value instead of standard deviation due to the fact that as the mean decreases, the standard deviation also decreases and vice versa. In other words, the standard deviation cannot be minimized first and the mean brought to the target. Taguchi has empirically found that the two stage optimization procedure involving S/N ratios indeed gives the parameter level combination, where the standard deviation is minimum while keeping the mean on target [13]. This implies that engineering systems behave in such a way that the manipulated production factors can be divided into three categories: 1. Control factors, which affect process variability as measured by the S/N ratio. 2. Signal factors, which do not influence the S/N ratio or process mean. 3. Factors, which do not affect the S/N ratio or process mean. In practice, the target mean value may change during the process development. Two of the applications in which the concept of S/N ratio is useful are the improvement of quality through variability reduction and the improvement of measurement. The S/N ratio characteristics can be divided into three categories when the characteristic is continuous:
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 91 Nominal is the best characteristic: ୗ ୒ ൌ 10 log ୷ഥ ୱ౯ మ (1) Smaller the better characteristics: ୗ ୒ ൌ െ10 log ଵ ୬ ሺ∑ yଶሻ (2) And Larger the better characteristics: ୗ ୒ ൌ െ10 log ଵ ୬ ቀ∑ ଵ ୷మቁ (3) where ‫ݕ‬ത is the average of observed data, s y the variance of y, n the number of observations, and y the observed data. For each type of the characteristics, with the above S/N ratio transformation, the higher the S/N ratio the better is the result. 2. DESIGN OF EXPERIMENT In this experiment with three factors at three levels each, the fractional factorial design used is a standard L27 (313) orthogonal array [1]. This orthogonal array is chosen due to its capability to check the interactions among factors. Each row of the matrix represents one trial. However, the sequence in which these trials are carried out is randomized. The three levels of each factor are represented by a ‘0’ or a ‘1’ or a ‘2’ in the matrix. The factors and levels are assigned as in Table 1 according to semi-finishing and finishing conditions for the said material when machining at high cutting speed. Factors A, B, and C are arranged in columns 2, 5 and 6, respectively, in the standard L27 (313) orthogonal array as shown in Appendix A. 2.1 Experimental Design and Setup The study was carried out using a 8-inch chuck class CNC lathe UNIVERSAL TYPE CNC TURNING MACHINE TU15, 12-corner tool rest with 12 pieces of tool attached with multiple tool change capabilities (max number of tools = 21) and with 15 HP spindle horsepower as shown in figure 2. The machine is capable of a three-axis movement (along the x, y, and z planes) Fast- forwarding speed: X-axis 20 m/min, Z-axis 24 m/min. CNC programs can be developed in the VMC CPU or downloaded from an external memory disc or data link. In this study, the CNC program was downloaded from an external memory disc. The machining trials were carried out on a chine in dry condition, as recommended. The machining trials were carried out in dry condition, as recommended by the tool supplier for the specific work material. Chip outlet can be selected based on a layout of machine. The surface roughness was measured using surface roughness tester model Mpi Mahr Perthometer. Table 1 shows the chemical composition of work material in percentage by weight.
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 92 Figure 1: Surface roughness profile Figure 2: Universal Type CNC Turning Machine Tu15 Table 1: Chemical composition of AISI 1040 in percentage by weight Elements Iron (Fe) Manganese (Mn) Carbon (C) Sulfur (S) Phosphorous (P) Contents (%) 98.6-99 0.60-0.90 0.37-0.44 < 0.05 < 0.04 Table 2: Estimated Minimum Values of Physical Properties of AISI1040 Tensile Strength, (psi) Yield Strength, (Psi) Elongation In 2in.,% Reduction In Area,% Brinell Hardness 76000 42000 18 40 149 2.2 Machining Operations and Machine Tools 1. Turning 2. Machining Turning –a machining process in which a single-point tool remove material from the surface of a rotating work piece (Lathe).
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 93 Rotational Speed ൌ ୚ πۭୈబ (4) D଴ െ D୤ ൌ 2d (5) Feed rate (f୰ ൌ N ൈ fሻ (6) Time of machining T୫ ൌ ୐ ୤౨ (7) Material removal rate MRR = vfd (8) Figure 3: Turning operations with parameters Cutting conditions Spindle Speed is given by N ൌ ୚ πۭୈ (9) Feed rate f୰ ൌ N ൈ f (10) Metal Removal Rate MRR ൌ πୈమ୤౨ ସ (11) Machining time T୫ ൌ t ൅ ୅ ୤౨ (For a through hole) (12) T୫ ൌ ୢ ୤౨ (For a blind hole) (13) Table 3: Factors and levels used in the experiment (axial depth of cut = 3 mm) Factor Level 0 1 2 A—speed (m/min) 200 300 400 B—feed rate (mm per tooth) 0.15 0.20 0.25 C—radial depth of cut (mm) 0.3 0.6 0.9 3. RESULTS AND DISCUSSION 3.1 Experimental results and data analysis The objective of experiment is to optimize the milling parameters to get better (i.e. low value) surface roughness and resultant force values, the smaller the better characteristics are used.
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 94 Table 4.2 shows the actual data for surface roughness along with their computed S/N ratio. Whereas Tables 5, 6 and 7 shows the mean S/N ratio for each levels of surface roughness for cutting speed at three different levels 0, 1 and 2 respectively. These data were then plotted as shown in fig. 4. 4.2 Conceptual S/N ratio approach Taguchi recommends analyzing the means and S/N ratio using conceptual approach that involves graphing the effects and visually identifying the factors that appear to be significant, without using ANOVA, thus making the analysis simple [3]. The average S/N ratios for smaller the better for surface roughness factors and their significant interactions are given in figs. 4 to 7. From the figures 4, 5, 6 and 7 it is observed that cutting speed (factor A) and interaction between feed rate and depth of cut (interaction B × C) are more significant compared to other two parameters. Feed rate (factor B) and depth of cut (factor C) are insignificant. The highest cutting speed (A0) appears to be the best choice to get low value of surface finish, and thus making the process robust to the cutting speed in particular. The feed rate (factor B) and depth of cut (factor C) are insignificant on the average S/N response. Table 4: Experimental results for surface roughness and S/N ratio Exp. Run Factor Designation Measured Parameters S/N Ratio A B C Surface roughness Ra (µm) 1 0 0 0 A଴B଴C଴ 0.213 13.432 2 0 1 1 A଴BଵCଵ 0.153 16.306 3 0 2 2 A଴BଶCଶ 0.643 3.835 4 1 0 0 AଵB଴C଴ 0.233 12.652 5 1 1 1 AଵBଵCଵ 0.215 13.351 6 1 2 2 AଵBଶCଶ 0.276 11.181 7 2 0 0 AଶB଴C଴ 0.590 4.582 8 2 1 1 AଶBଵCଵ 0.601 4.422 9 2 2 2 AଶBଶCଶ 0.689 3.235 10 0 0 1 A଴B଴Cଵ 0.248 12.110 11 0 1 2 A଴BଵCଶ 0.412 7.702 12 0 2 0 A଴BଶC଴ 0.493 6.143 13 1 0 1 AଵB଴Cଵ 0.269 11.404 14 1 1 2 AଵBଵCଶ 0.251 12.006 15 1 2 0 AଵBଶC଴ 0.633 3.971 16 2 0 1 AଶB଴Cଵ 0.703 3.060 17 2 1 2 AଶBଵCଶ 1.104 -0.859 18 2 2 0 AଶBଶC଴ 0.683 3.311 19 0 0 2 A଴B଴Cଶ 1.104 -1.385 20 0 1 0 A଴BଵC଴ 0.828 1.639 21 0 2 1 A଴BଶCଵ 0.931 0.621 22 1 0 2 AଵB଴Cଶ 1.687 -4.542 23 1 1 0 AଵBଵC଴ 1.021 -0.180 24 1 2 1 AଵBଶCଵ 0.919 0.733 25 2 0 2 AଶB଴Cଶ 1.522 -3.648 26 2 1 0 AଶB଴Cଶ 1.103 -0.851 27 2 2 1 AଶBଶCଵ 1.412 -2.996
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 95 Table 5: Experimental results for Mean S/N ratio for cutting speed at level 0 S. No. Mean S/N Ratio Mean S/N Ratio Calculated S/N Ratio Cutting Speed (A) Level 0 Cutting Speed (A) Level 1 Cutting Speed (A) Level 2 1 13.432 12.652 4.582 2 16.306 13.351 4.422 3 3.835 11.181 3.235 4 12.110 11.404 3.060 5 7.702 12.006 -0.859 6 6.143 3.971 3.311 7 -1.385 -4.542 -3.648 8 1.639 -0.180 -0.851 9 0.621 0.733 -2.996 Total 6.711 5.324 1.139 Figure 4: Smaller the better S/N graph for surface roughness at various cutting speed Table 6: Experimental results for Mean S/N ratio for Feed rate at level 0 S. No. Mean S/N Ratio Calculated S/N Ratio Calculated S/N Ratio Feed Rate (B) Level 0 Feed Rate (B) Level 1 Feed Rate (B) Level 2 1 13.432 16.306 3.835 2 12.652 13.351 11.181 3 4.582 4.422 3.235 4 12.110 7.702 6.143 5 11.404 12.006 3.971 6 3.060 -0.859 3.311 7 -1.385 1.639 0.621 8 -4.542 -0.180 0.733 9 -3.648 -0.851 -2.996 Total 5.296 5.948 3.337 Cutting Speed (m/min) 0 2 4 6 8 1 2 3 MeanS/NRatio The Smaller the better S/N graph
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 96 Figure 5: The smaller the better S/N graph for surface roughness at various feed rate Table 7: Experimental results for Mean S/N ratio for Depth of cut at level 0 S. No. Mean S/N Ratio Mean S/N Ratio Mean S/N Ratio Depth of Cut (C) Level 0 Depth of Cut (C) Level 1 Depth of Cut (C) Level 2 1 13.432 16.306 3.835 2 12.652 13.351 11.181 3 4.582 4.422 3.235 4 6.143 12.110 7.702 5 3.971 11.404 12.006 6 3.311 3.060 -0.859 7 1.639 0.621 -1.385 8 -0.180 0.733 -4.542 9 -0.851 -2.996 -3.648 Total 4.966 6.556 3.058 Figure 6: The smaller the better S/N graph for surface roughness at various depth of cut Radial depth of cut (mm) 0 2 4 6 8 1 2 3 MeanS/Nratio The smaller the better S/N ratio for surface roughness Feed Rate (mm/tooth) 0 1 2 3 4 5 6 7 1 2 3 MeanS/Nratio Smaller the Better S/N graph for surface roughness
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 97 Table 8: Experimental results for Mean S/N ratio for interaction (BXC) at level 0 S. No. Mean S/N Ratio Mean S/N Ratio Mean S/N Ratio Interaction (BXC) Level 0 Interaction (BXC) Level 1 Interaction (BXC) Level 2 1 13.432 12.110 -1.385 2 16.306 7.702 1.639 3 3.835 6.143 0.621 4 12.652 11.404 -4.542 5 13.351 12.006 -0.180 6 11.181 3.971 0.733 7 4.582 3.060 -3.648 8 4.422 -0.859 -0.851 9 3.235 3.311 -2.996 Total 9.221 6.538 -1.178 Figure 7: The smaller the better S/N graph for surface roughness interaction between feed rate and depth of cut (BXC) Table 9: Response table for average S/N ratio for surface roughness factors and significant interaction Cutting Parameters Max-Min Net Value Cutting Speed (A) 6.711-1.139 5.572 Feed Rate (B) 5.948-3.337 2.611 Depth of Cut (C) 6.556-3.058 3.498 BXC 9.221 – (-1.178) 10.399 The use of S/N ratio for selecting the best levels of combination for surface roughness (Ra) value suggests the use of low value of feed rate in order to obtain good finish. Smaller angle of tool angular position is obtained at lower depth of cut [15]. Therefore, it is preferable to set the depth of cut to a low value. Therefore, one can say that the set values for level ‘0’ and ‘1’ are both suitable to obtain good quality of surface finish. From the result, the interaction of factor B and factor C is more important than the effect of the individual factors. In other words, in order to get the best result it requires experience to combine these two factors to achieve a suitable combination of feed rate and depth of cut. The S/N ratio suggests that cutting force depends on feed rate and depth of cut. Both the feed rate and depth of cut are found to be at level ‘0’ for the best combination to obtain low value of (BXC) -2 0 2 4 6 8 10 MeanS/NRatio The Smaller the better S/N ratio for surface roughness
  10. 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 98 surface roughness. The combination of feed rate and depth of cut determines the undeformed chip section and hence the amount of energy required to remove a specified volume of material. Appendix A L27 (313 ) standard orthogonal array table with factors A, B and C arranged in column 2, 5 and 6 respectively. The interactions among factors are indicated as in columns 1, 7, 8, 9, 11 and 12. Exp. Run 1 2 3 4 5 6 7 8 9 10 11 12 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 1 1 1 1 1 1 1 3 0 0 0 0 2 2 2 2 2 2 2 2 2 4 0 1 1 1 0 0 0 1 1 1 2 2 2 5 0 1 1 1 1 1 1 2 2 2 0 0 0 6 0 1 1 1 2 2 2 0 0 0 1 1 1 7 0 2 2 2 0 0 0 2 2 2 1 1 1 8 0 2 2 2 1 1 1 0 0 0 2 2 2 9 0 2 2 2 2 2 2 1 1 1 0 0 0 10 1 0 1 2 0 1 2 0 1 2 0 1 2 11 1 0 1 2 1 2 0 1 2 1 1 2 0 12 1 0 1 2 2 0 1 2 0 0 2 0 1 13 1 1 2 0 0 1 2 1 2 0 2 0 1 14 1 1 2 0 1 2 0 2 0 1 0 1 2 15 1 1 2 0 2 0 1 0 1 2 1 2 0 16 1 2 0 1 0 1 2 2 0 1 1 2 0 17 1 2 0 1 1 2 0 0 1 2 2 0 1 18 1 2 0 1 2 0 1 1 2 0 0 1 2 19 2 0 2 0 0 2 1 0 2 1 0 2 1 20 2 0 2 0 1 0 2 1 0 2 1 0 2 21 2 0 2 0 2 1 0 2 1 0 2 1 0 22 2 1 0 1 0 2 1 1 0 2 2 1 0 23 2 1 0 1 1 0 2 2 1 0 0 2 1 24 2 1 0 1 2 1 0 0 2 1 1 0 2 25 2 2 1 2 0 2 1 2 1 0 1 0 2 26 2 2 1 2 1 0 2 0 2 1 2 1 0 27 2 2 1 2 2 1 0 1 0 2 0 2 1 BXC A B C BXC AXB AXC AXB AXC CONCLUSION From the analysis of result in turning process using conceptual S/N ratio approach the following can be concluded from the present study that the Taguchi’s robust design method is suitable to analyze the metal cutting problem as described in this work. Conceptual S/N ratio approaches for data analysis has been used and it gives the similar results published in other literatures. In turning operation, use of high cutting speed (400 m/min), low feed rate (0.15mm per tooth) and low depth of cut (0.3 mm) are recommended to obtain better surface finish for the specific test range. Generally, the use of high cutting speed, low feed rate and low depth of cut leads to better surface finish.
  11. 11. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME 99 REFERENCES [1] Mohd Sazali Md Said et al., “Comparison between Taguchi Method and Response Surface Methodology (RSM) In Optimizing Machining Condition”, International Conference on Robust Quality Engineering [2013]. [2] Adem Çiçek et al., “Application of Taguchi Method for Surface Roughness and Roundness Error in Drilling of AISI 316 Stainless Steel”, Journal of Mechanical Engineering 58 (2012) 3, 165-174 [3] Amit Joshi and Pradeep Kothiyal, “Investigating Effect of Machining Parameters of CNC Milling on Surface Finish by Taguchi Method”, International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME) Volume-1, Issue-2, 2012 [4] Show-Shyan Lin et al., “Optimization of 6061T6 CNC Boring Process Using the Taguchi Method and Grey Relational Analysis”, The Open Industrial and Manufacturing Engineering Journal, 2009, 2, 14- 20. [5] Jignesh G.Parmar and Alpesh Makwana, “PREDICTION OF SURFACE ROUGHNESS FOR END MILLING PROCESS USING ARTIFICIAL NEURAL NETWORK”, International Journal of Advanced Engineering Research and Studies [2010] [6] R. Horváth and Á. Drégelyi-Kiss, “Analysis of Surface Roughness Parameters in Aluminium Fine Turning with Diamond Tool”, MEASUREMENT 2013, Proceedings of the 9th International Conference, Smolenice, Slovakia [7] Sanjit Moshat et al., “Optimization of CNC end milling process parameters using PCA-based Taguchi method”, International Journal of Engineering, Science and Technology, Vol. 2, No. 1, 2010, pp. 92- 102. [8] R. Arokiadass et al., “Surface roughness prediction model in end milling of Al/SiCp MMC by carbide tools”, International Journal of Engineering, Science and Technology Vol. 3, No. 6, 2011, pp. 78-87 [9] Than Tun Aung et al., “A Study of Surface Roughness caused by Conventional and High Speed Machining in Side Milling Operation”, Technological Research Department, Myanmar. [10] Srinivas Athreya and Y. D. Venkatesh, “Application Of Taguchi Method For Optimization Of Process Parameters In Improving The Surface Roughness Of Lathe Facing Operation”, International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 1, Issue 3 (November 2012), PP.13-19. [11] Lokeswara Rao T and N. Selvaraj, “Optimization of WEDM Process Parameters on Titanium Alloy Using Taguchi Method”, International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol. 3, Issue. 4, Jul. - Aug. 2013 pp-2281-2286 [12] S. S. Chaudhari et al., “Optimization of process parameters using Taguchi approach with minimum quantity lubrication for turning”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 1, Issue 4, pp.1268-1273. [13] A. K. Nachimuthu, “Minimization of Surface Roughness in CNC Turning Using Taguchi Method”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) e-ISSN: 2278-1684,p-ISSN: 2320- 334X, Volume 8, Issue 1 (Jul. - Aug. 2013), PP 47-49. [14] B. Fnides et al., “Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5- 1 using mixed ceramic”, MECHANIKA. 2008. Nr. 2 (70). [15] M. Kaladhar et al., “Application of Taguchi approach and Utility Concept in solving the Multi- objective Problem when turning AISI 202 Austenitic Stainless Steel”, Journal of Engineering Science and Technology Review 4 (1) (2011) 55-61. [16] Kareem Idan Fadheel and Dr. Mohammad Tariq, “Optimization of End Milling Parameters of AISI 1055 by Taguchi Method”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 5, Issue 3, 2014, pp. 9 - 20, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [17] S. Madhava Reddy, “Optimization of Surface Roughness in High-Speed End Milling Operation using Taguchi’s Method”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 4, 2013, pp. 249 - 258, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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