Research Journal of Engineering Sciences ___________________________________________ ISSN 2278 – 9472Vol. 2(9), 1-6, September (2013) Res. J. Engineering Sci. International Science Congress Association 1 Influence of Cutting Parameters on Turning Process Using Anova Analysis D.V.V. Krishan PrasadR.V.R. and J.C. College of Engineering Guntur, INDIA Available online at: www.isca.in Received 30th Aguest 2013, revised 10th September 2013, accepted 23rd Septeber 2013 Abstract In a turning process surface roughness depend on machining parameters and tool geometry. In this work considering three machining parameters and two tool geometrical parameters 243 experiments were conducted for full factorial design. Using ANOVA analysis the influence of these parameters on surface roughness was studied. Keywords: Optimization, machining parameters, ANOVA, rake angles, surface finish. Introduction In Single pass turning the conditions during cutting such as cutting speed, feed rate and depth of cut should be selected to optimize the surface roughness. The selection to efficient machining parameters such as machining speed, feed rate and depth of cut has a direct impact in the metal cutting processes. The cutting tool geometry such as back rake, side rake also slightly affects the surface roughness. The major efforts of earlier works were concentrated on optimization of machining parameters and not concentrated on geometry of cutting. In this work we tried to find influence of machining parameters and tool geometry on surface roughness. Machining parameters in turning process: In metal cutting, there are many factors related to process planning for machining operations. These factors can be classified as: i. Type of machining operations (turning, facing, milling, etc.), ii. Parameters of machine tools (rigidity, horse power, etc.), iii. Parameters of cutting tools (material, geometry, etc.), iv. Parameters of cutting conditions (cutting speed, feed rate, depth of cut, etc.), v. Characteristics of work pieces (material, geometry, etc.). Among these factors cutting parameters (speed, feed rate and depth of cut) and tool geometry (back rake, side rake) are evidently dominating ones in a machining operation.Cutting Speed (): The cutting speed of a tool is the speed at which the metal is removed by the tool from the work material. In a lathe it is the peripheral speed of the work part in m/min. = DN/1000 (m/min) Where D, N are diameter of work piece (mm) and cutting speed (rpm) respectively. Feed (): The feedof the cutting tool in lathe work is the distance, the tool advances for each revolution of the work piece in mm. Depth of cut (): The depth of cut is the perpendicular distance measured from the machined surface to the uncut surface of the work piece in mm. Back rake angle: It is the angle provided from the cutting edge to the shank of a single point cutting tool, back rake is to help control the direction of the chip, which naturally curves into the work due to the difference in length from the outer and inner parts of the cut. It also helps counteract the pressure against the tool from the work by pulling the tool into the work piece. 5. Side rake angle: It is the angle provided between front face to the side of the single point cutting tool. Figure-1 Two rake angles of single point cutting tool Side rake along with back rake controls the chip flow and partly counteracts the resistance of the work to the movement of the cutter and can be optimized to suit the particular material being cut. Research Journal of Engineering Sciences________________________________________________________ ISSN 2278 – 9472 Vol. 2(9), 1-6, September (2013) Res. J. Engineering Sci. International Science Congress Association 2 For a given machining operation determination of the optimum cutting conditions involves a conflict between maximizing the material removal rate and minimizing the surface roughness. The machining process optimization is to determine the most advantageous cutting condition. This is to determine optimal machining parameters such as v (cutting speed), f (feed rate), d (depth of cut) and the tool geometry back rake, side rake to optimize specified objectives such as surface roughness and MRR. Literature-Survey: Gilbert1 optimized of machining parameters in turning operation by considering maximum production rate and minimum production as objective functions. By expressing the production cost and production rate in terms of speed and feed rate Armaregoand Brown and partially differentiating these terms with respect to speed and feed and equating to zero the optimum cutting conditions are obtained. Brewer and Rueda3 obtained number of nomograms for facilitating the determination of the economic machining conditions by employing the criterion of reducing the machining cost to a minimum for cast iron and steels. The usage of geometric programming for selection of machining variables were studied by Walvekar and Lambertandobtained optimized cutting speed and feed rate to optimize the production cost. By geometric programming Optimal selection of machining rate variables, was investigated by Petropoulos . A constrained unit cost problem in turning was optimized by using carbide tipped for machining SAE 1045 steel using a goal-programming technique in metal cutting for selecting levels of machining parameters in a fine turning operation on AISI 4140 steel using cemented tungsten carbide tools was studied by Sundaram. constrained multi-pass machining problem was studied by Ermerand Kromodiharajoand concluded, multi-pass machining was more economical than single-pass machining if depth of cut for each pass was properly allocated. Hindujaet al considered minimum cost or maximum production rate as the objective function and calculated the optimum cutting conditions for turning operation for a given combination of tool and work material, considered surface finish, dimensional accuracy and power available as constraints. Lambert and Taramandeveloped a mathematical model to evaluate the cutting force for turning SAE 1018 cold-rolled steel with a carbide tool and utilized the model in the selection of levels of the machining variables such that the material removal rate could be at the highest possible value without violating the given force restrictions. Hassan and Suliman10 presented mathematical models for the prediction of cutting time, surface roughness, tool vibration, power consumption, while using tungsten carbide tools for turning medium carbon steel under dry conditions. El Baradie11 developed of a surface roughness model while using tipped carbide tools for turning grey cast iron under dry conditions and with constant depth of cut. The mathematical model is utilizing the response surface methodology was developed in terms of cutting speed, feed rate and nose radius of the cutting tool. These variables were investigated using design of experiments and utilization of the response surface methodology. Using of goal programming technique in for single pass turning operation. T.S. Sidhu12 determined optimum values for speed and feed by setting different goals for a given set of conditions. Yen and Wright13 developed a unified method of adaptive control of constraints in which a suitable cutting region is determined satisfying all the physical constraints. The objective of the optimization is to maximize the production rate under constraints of plastic deformation, crater wear and fracture. Problem: To find the optimum parameters in order to get the minimum surface roughness and to analyze the effect of machining parameters and rake angles on the surface finish. Design of experiments is the most useful and effective statistical quality control technique to investigate and individual interaction effects of process parameters. It isolates the effect of each input variable. Full factorial experiments consist of possible combinations of the levels of factors. Turning operation was carried out at 3 levels of the back rake, side rake; speed feed and depth of cut the range of parameters are shown in table 1 Table-1 Three levels with five parameters (3^5 factors design) Levels Rake Angle (º) Speed (rpm) Feed (mm/rev) Depth of cut (mm) Back Side -1 8 12 250 0.1 0.5 0 11 15 350 0.3 1 1 14 18 550 0.5 1.5 Experimental Procedure: In this work mild steel is selected as the specimen, since it is mostly used structural steel. A mild steel rod of length around 20 ft has taken for this experiment. The lengthy rod was cut into 41 pieces as per required specifications. The Specifications used are ( \n  for the specimen with the cutting tool as high speed steel. On each work piece turning operation is performed for three variables like that 243 experiments are conducted on this 41 work pieces. Surface finish is measured using TALYSURF for 243 experiments. Results are tabulated in table 2 Table-2 Surface roughness at various cutting speeds S. No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) Back Side 1 8 12 250 0.1 0.5 2.835 2 8 12 250 0.1 1.0 3.190 3 8 12 250 0.1 1.5 3.510 4 8 12 250 0.3 0.5 3.810 5 8 12 250 0.3 1.0 3.405 6 8 12 250 0.3 1.5 4.390 7 8 12 250 0.5 0.5 4.270 8 8 12 250 0.5 1.0 4.210 9 8 12 250 0.5 1.5 4.410 Research Journal of Engineering Sciences________________________________________________________ ISSN 2278 – 9472 Vol. 2(9), 1-6, September (2013) Res. J. Engineering Sci. International Science Congress Association 3 S. No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) 10 8 12 350 0.1 0.5 2.265 11 8 12 350 0.1 1.0 2.545 12 8 12 350 0.1 1.5 2.475 13 8 12 350 0.3 0.5 3.200 14 8 12 350 0.3 1.0 3.160 15 8 12 350 0.3 1.5 3.720 16 8 12 350 0.5 0.5 4.345 17 8 12 350 0.5 1.0 3.890 18 8 12 350 0.5 1.5 4.600 19 8 12 550 0.1 0.5 2.200 20 8 12 550 0.1 1.0 1.665 21 8 12 550 0.1 1.5 2.080 22 8 12 550 0.3 0.5 2.940 23 8 12 550 0.3 1.0 2.410 24 8 12 550 0.3 1.5 2.885 25 8 12 550 0.5 0.5 3.765 26 8 12 550 0.5 1.0 3.735 27 8 12 550 0.5 1.5 4.170 28 8 15 250 0.1 0.5 3.050 29 8 15 250 0.1 1.0 2.750 30 8 15 250 0.1 1.5 3.150 31 8 15 250 0.3 0.5 3.605 32 8 15 250 0.3 1.0 3.710 33 8 15 250 0.3 1.5 3.830 34 8 15 250 0.5 0.5 4.125 35 8 15 250 0.5 1.0 4.230 36 8 15 250 0.5 1.5 5.030 37 8 15 350 0.1 0.5 2.485 38 8 15 350 0.1 1.0 2.570 39 8 15 350 0.1 1.5 2.800 40 8 15 350 0.3 0.5 3.385 41 8 15 350 0.3 1.0 2.920 42 8 15 350 0.3 1.5 3.430 43 8 15 350 0.5 0.5 3.960 44 8 15 350 0.5 1.0 4.155 45 8 15 350 0.5 1.5 4.230 46 8 15 550 0.1 0.5 2.145 47 8 15 550 0.1 1.0 2.060 48 8 15 550 0.1 1.5 2.425 49 8 15 550 0.3 0.5 2.600 50 8 15 550 0.3 1.0 2.670 51 8 15 550 0.3 1.5 3.160 52 8 15 550 0.5 0.5 3.870 53 8 15 550 0.5 1.0 3.580 54 8 15 550 0.5 1.5 4.150 55 8 18 250 0.1 0.5 2.905 56 8 18 250 0.1 1.0 2.605 57 8 18 250 0.1 1.5 3.195 58 8 18 250 0.3 0.5 3.380 59 8 18 250 0.3 1.0 3.225 60 8 18 250 0.3 1.5 3.740 S. No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) 61 8 18 250 0.5 0.5 4.320 62 8 18 250 0.5 1.0 4.445 63 8 18 250 0.5 1.5 5.110 64 8 18 350 0.1 0.5 2.415 65 8 18 350 0.1 1.0 2.200 66 8 18 350 0.1 1.5 2.510 67 8 18 350 0.3 0.5 3.080 68 8 18 350 0.3 1.0 2.550 69 8 18 350 0.3 1.5 3.210 70 8 18 350 0.5 0.5 4.120 71 8 18 350 0.5 1.0 3.730 72 8 18 350 0.5 1.5 3.925 73 8 18 550 0.1 0.5 2.115 74 8 18 550 0.1 1.0 1.795 75 8 18 550 0.1 1.5 2.255 76 8 18 550 0.3 0.5 2.800 77 8 18 550 0.3 1.0 2.640 78 8 18 550 0.3 1.5 3.440 79 8 18 550 0.5 0.5 3.825 80 8 18 550 0.5 1.0 3.480 81 8 18 550 0.5 1.5 4.105 82 11 12 250 0.1 0.5 2.840 83 11 12 250 0.1 1.0 2.805 84 11 12 250 0.1 1.5 3.190 85 11 12 250 0.3 0.5 3.615 86 11 12 250 0.3 1.0 3.590 87 11 12 250 0.3 1.5 3.725 88 11 12 250 0.5 0.5 4.450 89 11 12 250 0.5 1.0 4.075 90 11 12 250 0.5 1.5 5.065 91 11 12 350 0.1 0.5 2.230 92 11 12 350 0.1 1.0 1.795 93 11 12 350 0.1 1.5 2.570 94 11 12 350 0.3 0.5 2.760 95 11 12 350 0.3 1.0 2.830 96 11 12 350 0.3 1.5 3.360 97 11 12 350 0.5 0.5 3.960 98 11 12 350 0.5 1.0 3.355 99 11 12 350 0.5 1.5 4.340 100 11 12 550 0.1 0.5 2.110 101 11 12 550 0.1 1.0 2.035 102 11 12 550 0.1 1.5 2.440 103 11 12 550 0.3 0.5 2.840 104 11 12 550 0.3 1.0 2.635 105 11 12 550 0.3 1.5 3.375 106 11 12 550 0.5 0.5 3.590 107 11 12 550 0.5 1.0 3.725 108 11 12 550 0.5 1.5 3.980 109 11 15 250 0.1 0.5 2.335 110 11 15 250 0.1 1.0 2.290 111 11 15 250 0.1 1.5 3.040 Research Journal of Engineering Sciences________________________________________________________ ISSN 2278 – 9472 Vol. 2(9), 1-6, September (2013) Res. 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No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) 112 11 15 250 0.3 0.5 3.480 113 11 15 250 0.3 1.0 3.405 114 11 15 250 0.3 1.5 3.780 115 11 15 250 0.5 0.5 4.725 116 11 15 250 0.5 1.0 4.410 117 11 15 250 0.5 1.5 4.850 118 11 15 350 0.1 0.5 2.145 119 11 15 350 0.1 1.0 1.925 120 11 15 350 0.1 1.5 2.480 121 11 15 350 0.3 0.5 3.210 122 11 15 350 0.3 1.0 2.940 123 11 15 350 0.3 1.5 3.220 124 11 15 350 0.5 0.5 3.795 125 11 15 350 0.5 1.0 3.640 126 11 15 350 0.5 1.5 4.440 127 11 15 550 0.1 0.5 2.040 128 11 15 550 0.1 1.0 2.110 129 11 15 550 0.1 1.5 2.260 130 11 15 550 0.3 0.5 3.040 131 11 15 550 0.3 1.0 2.560 132 11 15 550 0.3 1.5 3.080 133 11 15 550 0.5 0.5 3.220 134 11 15 550 0.5 1.0 3.375 135 11 15 550 0.5 1.5 3.900 136 11 18 250 0.1 0.5 2.815 137 11 18 250 0.1 1.0 2.600 138 11 18 250 0.1 1.5 3.275 139 11 18 250 0.3 0.5 3.440 140 11 18 250 0.3 1.0 3.250 141 11 18 250 0.3 1.5 3.870 142 11 18 250 0.5 0.5 4.465 143 11 18 250 0.5 1.0 4.210 144 11 18 250 0.5 1.5 4.700 145 11 18 350 0.1 0.5 2.350 146 11 18 350 0.1 1.0 2.475 147 11 18 350 0.1 1.5 2.580 148 11 18 350 0.3 0.5 3.345 149 11 18 350 0.3 1.0 2.580 150 11 18 350 0.3 1.5 3.555 151 11 18 350 0.5 0.5 3.750 152 11 18 350 0.5 1.0 3.740 153 11 18 350 0.5 1.5 3.825 154 11 18 550 0.1 0.5 2.040 155 11 18 550 0.1 1.0 1.880 156 11 18 550 0.1 1.5 2.280 157 11 18 550 0.3 0.5 3.170 158 11 18 550 0.3 1.0 2.205 159 11 18 550 0.3 1.5 3.215 160 11 18 550 0.5 0.5 3.425 161 11 18 550 0.5 1.0 3.440 162 11 18 550 0.5 1.5 3.815 S. No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) 163 14 12 250 0.1 0.5 2.195 164 14 12 250 0.1 1.0 2.320 165 14 12 250 0.1 1.5 3.120 166 14 12 250 0.3 0.5 3.120 167 14 12 250 0.3 1.0 3.150 168 14 12 250 0.3 1.5 3.340 169 14 12 250 0.5 0.5 4.170 170 14 12 250 0.5 1.0 3.710 171 14 12 250 0.5 1.5 4.320 172 14 12 350 0.1 0.5 2.250 173 14 12 350 0.1 1.0 1.780 174 14 12 350 0.1 1.5 2.360 175 14 12 350 0.3 0.5 2.670 176 14 12 350 0.3 1.0 2.480 177 14 12 350 0.3 1.5 3.115 178 14 12 350 0.5 0.5 3.560 179 14 12 350 0.5 1.0 3.345 180 14 12 350 0.5 1.5 3.915 181 14 12 550 0.1 0.5 1.890 182 14 12 550 0.1 1.0 1.580 183 14 12 550 0.1 1.5 1.970 184 14 12 550 0.3 0.5 2.740 185 14 12 550 0.3 1.0 2.400 186 14 12 550 0.3 1.5 2.665 187 14 12 550 0.5 0.5 3.175 188 14 12 550 0.5 1.0 2.885 189 14 12 550 0.5 1.5 3.400 190 14 15 250 0.1 0.5 2.465 191 14 15 250 0.1 1.0 2.560 192 14 15 250 0.1 1.5 2.435 193 14 15 250 0.3 0.5 3.335 194 14 15 250 0.3 1.0 2.920 195 14 15 250 0.3 1.5 3.045 196 14 15 250 0.5 0.5 3.920 197 14 15 250 0.5 1.0 3.400 198 14 15 250 0.5 1.5 4.120 199 14 15 350 0.1 0.5 1.460 200 14 15 350 0.1 1.0 1.370 201 14 15 350 0.1 1.5 2.060 202 14 15 350 0.3 0.5 2.275 202 14 15 350 0.3 1.0 2.420 204 14 15 350 0.3 1.5 2.800 205 14 15 350 0.5 0.5 3.385 206 14 15 350 0.5 1.0 3.205 207 14 15 350 0.5 1.5 3.640 208 14 15 550 0.1 0.5 1.700 209 14 15 550 0.1 1.0 1.605 210 14 15 550 0.1 1.5 1.925 211 14 15 550 0.3 0.5 2.460 212 14 15 550 0.3 1.0 2.555 213 14 15 550 0.3 1.5 2.420 Research Journal of Engineering Sciences________________________________________________________ ISSN 2278 – 9472 Vol. 2(9), 1-6, September (2013) Res. J. Engineering Sci. International Science Congress Association 5 S. No. Rake angle (°) Peed (rpm) Feed (mm/rev) Doc (mm) Surface Roughness (µm) 214 14 15 550 0.5 0.5 2.970 215 14 15 550 0.5 1.0 2.720 216 14 15 550 0.5 1.5 3.050 217 14 18 250 0.1 0.5 2.310 218 14 18 250 0.1 1.0 2.090 219 14 18 250 0.1 1.5 2.669 220 14 18 250 0.3 0.5 2.760 221 14 18 250 0.3 1.0 3.075 222 14 18 250 0.3 1.5 3.570 223 14 18 250 0.5 0.5 3.545 224 14 18 250 0.5 1.0 3.715 225 14 18 250 0.5 1.5 4.200 226 14 18 350 0.1 0.5 1.920 227 14 18 350 0.1 1.0 1.285 228 14 18 350 0.1 1.5 2.085 229 14 18 350 0.3 0.5 2.540 230 14 18 350 0.3 1.0 2.640 231 14 18 350 0.3 1.5 3.120 232 14 18 350 0.5 0.5 3.775 233 14 18 350 0.5 1.0 3.375 234 14 18 350 0.5 1.5 3.720 235 14 18 550 0.1 0.5 1.630 236 14 18 550 0.1 1.0 1.465 237 14 18 550 0.1 1.5 2.225 238 14 18 550 0.3 0.5 2.725 239 14 18 550 0.3 1.0 2.380 240 14 18 550 0.3 1.5 2.570 241 14 18 550 0.5 0.5 3.200 242 14 18 550 0.5 1.0 3.060 243 14 18 550 0.5 1.5 3.390 Results ANOVA analysis is carried out on the data shown in table1 using MINITAB Software for surface roughness and results are tabulated in table3 Table-3 Analysis of variance for surface roughness Source DF Seq SS Adj SS Adj MS F P Back Rake 2 12.52 12.5 6.263 155.2 8.1 Side Rake 2 0.378 0.38 0.189 4.69 0.2 Speed 2 23.69 23.7 11.84 293.6 15.3 Feed 2 99.726 99.7 49.83 1236 64.3 DOC 2 9.355 9.35 4.677 115.9 6.05 Error 232 9.359 9.35 0.040 6.05 Total 242 155.03 S = 0.200851 R-Sq = 93.96% R-Sq(adj) = 93.70% Main effects plot and interaction plot are shown in figure .2 and figure-3 Conclusion From figure-2it is observed that minimum surface roughness is obtained at a speed of 550 rpm, feed of 0.1 mm/rev, depth of cut of 1mm, side rake angle of 18º and back rake angle of 14º the surface finish is 1.465µm. From table3 it is observed that feed is the significant parameter influencing surface roughness and side rake angle is having very less effect on surface roughness        \n \r\n  \r\n     \n  \r\n\n\n\r\nFigure-2 Main Effects Plot          \n  \n \n   \n \n \r\n\n\n\r\nFigure-3 Interaction Plot References 1.Gilbert W.W., Economics of Machining. In Machining – Theory and Practice, Am.Soc.Met., 476-480, (1950)2.Armarego and Brown., The Machining of metals, Prentice Hall Inc, Englewood Cliffs, New Jersey, (1980)3.Brewer R.C. and Rueda R., A Simplified Approach to the Optimum Selection of Machining Parameters, Eng.Dig.,24(9), 133-150 (1963) Research Journal of Engineering Sciences________________________________________________________ ISSN 2278 – 9472 Vol. 2(9), 1-6, September (2013) Res. J. Engineering Sci. International Science Congress Association 6 4.Walvekar A.G. and Lambert B.K., An Application of Geometric Programming to Machining Variable Selection, Int.J.Prod. 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