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Optimization of cutting parameters in dry turning operation of mild steel
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Optimization of cutting parameters in dry turning operation of mild steel
1.
International Journal of
Advanced JOURNAL OF ADVANCED RESEARCH IN0976 – INTERNATIONAL Research in Engineering and Technology (IJARET), ISSN 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December(IJARET) ENGINEERING AND TECHNOLOGY (2012), © IAEME ISSN 0976 - 6480 (Print) IJARET ISSN 0976 - 6499 (Online) Volume 3, Issue 2, July-December (2012), pp. 104-110 © IAEME: www.iaeme.com/ijaret.html ©IAEME Journal Impact Factor (2012): 2.7078 (Calculated by GISI) www.jifactor.com OPTIMIZATION OF CUTTING PARAMETERS IN DRY TURNING OPERATION OF MILD STEEL RAHUL DAVIS 1* 1* Assistant Professor, Department of Mechanical Engineering and Applied Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India E-mail: rahuldavis2012@gmail.com MOHAMED ALAZHARI 2 2 Assistant Professor, Department of Mechanical Engineering Aljabal Algarby University Hai Alandolas, Main Street, Tripoli, Libya E-mail: tobzal@yahoo.com ABSTRACT The quality of machined surface is characterized by the accuracy of its manufacture with respect to the dimensions specified by the designer. Therefore it becomes necessary to get the required surface quality in safe zone to have the choice of optimized cutting factors. In the proposed research work the cutting parameters (depth of cut, feed rate, spindle speed) have been optimized in dry turning of mild steel of (0.21% C) in turning operations on mild steel by high speed steel cutting tool in dry condition and as a result of that the combination of the optimal levels of the factors was obtained to get the lowest surface roughness. The Analysis of Variance (ANOVA) and Signal-to-Noise ratio were used to study the performance characteristics in turning operation. The results of the analysis show that depth of cut was the only parameter found to be significant. Results obtained by Taguchi method match closely with ANOVA and depth of cut is most influencing parameter. The analysis also shows that the predicted values and calculated values are very close, that clearly indicates that the developed model can be used to predict the surface roughness in the turning operation of mild steel. Keywords: Mild steel, Dry turning, Surface Roughness, Taguchi Method 1. INTRODUCTION Product designers constantly strive to design machinery that can run faster, last longer, and operate more precisely than ever. Modern development of high speed machines has resulted in higher loading and increased speeds of moving parts. Bearings, seals, shafts, machine ways, and gears, for example must be accurate - both dimensionally and geometrically. 104
2.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Unfortunately, most manufacturing processes produce parts with surfaces that are either unsatisfactory from the standpoint of geometrical perfection or quality of surface texture. This primer begins by explaining how industry controls and measures the precise degree of smoothness and roughness of a finished surface.1 Mild steel has a relatively low tensile strength, but it is cheap and malleable, surface hardness can be increased through carburizing. Carbon content makes mild steel malleable and ductile, but it cannot be hardened by heat treatment2. Since Turning is the primary operation in most of the production process in the industry, surface finish of turned components has greater influence on the quality of the product3. Surface finish in turning has been found to be influenced in varying amounts by a number of factors such as feed rate, work hardness, unstable built up edge, speed, depth of cut, cutting time, use of cutting fluids etc4. There are three primary input control parameters in the basic turning operations. They are feed, spindle speed and depth of cut. Feed is the rate at which the tool advances along its cutting path. Speed always refers to the spindle and the work piece. Depth of cut is the thickness of the material that is removed by one pass of the cutting tool over the workpiece5. 2. MATERIALS AND METHODS The present research work reflects the usage of L27 Taguchi orthogonal design6 as the study the effect of three different parameters (depth of cut, feed & spindle speed) on the surface roughness of the specimens of mild steel was aimed after turning operations were done 27 times in the Students Workshop in the Department of Mechanical Engineering, Shepherd School of Engineering and Technology, SHIATS, Allahabad (U.P.), India, followed by measurements of surface roughness around the part with the help of workpiece fixture and the measurements of surface roughness were taken across the lay, while the setup was a three-jaw chuck in Sparko Engineering Workshop, Allahabad (U.P.) India. The total length of the workpiece (152.4 mm) was divided into 6 equal parts and the surface roughness measurements were taken of each 25.4 mm around each workpiece. The turning operations were performed by high speed steel cutting tool in dry cutting condition. Mild steel with carbon (0.21%), manganese (0.64 %) was selected as the specimen material. The values of the three input control parameters for the Turning Operation are as under: Table: 2.1 Details of the Turning Operation Factors Level 1 Level 2 Level 3 Depth of cut (mm) 0.5 1.0 1.5 Feed Rate (mm/rev) 0.002 0.011 0.020 Spindle Speed (rpm) 14.91 25.12 40.03 105
3.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Table 2.2: Results of Experimental Trial Runs for Turning Operation Experiment Depth Feed Spindle Speed Surface SN Ratio No. of Cut Rate (rpm) Roughness (mm) (mm/rev) (µm) 1 0.5 0.002 14.91 10.040 -20.0347 2 0.5 0.002 25.12 3.700 -11.3640 3 0.5 0.002 40.03 16.930 -24.5731 4 0.5 0.011 14.91 9.330 -19.3976 5 0.5 0.011 25.12 1.910 -5.6207 6 0.5 0.011 40.03 11.010 -20.8357 7 0.5 0.020 14.91 14.590 -23.2811 8 0.5 0.020 25.12 4.020 -12.0845 9 0.5 0.020 40.03 1.880 -5.4832 10 1.0 0.002 14.91 31.250 -29.8970 11 1.0 0.002 25.12 26.750 -28.5465 12 1.0 0.002 40.03 43.370 -32.7438 13 1.0 0.011 14.91 30.710 -29.7456 14 1.0 0.011 25.12 15.610 -23.8681 15 1.0 0.011 40.03 29.620 -29.4317 16 1.0 0.020 14.91 35.620 -31.0339 17 1.0 0.020 25.12 45.331 -33.1279 18 1.0 0.020 40.03 27.040 -28.6401 19 1.5 0.002 14.91 21.250 -26.5472 20 1.5 0.002 25.12 63.040 -35.9923 21 1.5 0.002 40.03 78.120 -37.8552 22 1.5 0.011 14.91 71.480 -37.0837 23 1.5 0.011 25.12 54.780 -34.7724 24 1.5 0.011 40.03 79.180 -37.9723 25 1.5 0.020 14.91 49.570 -33.9044 26 1.5 0.020 25.12 45.950 -33.2457 27 1.5 0.020 40.03 64.250 -36.1575 In the present experimental work, the assignment of factors was carried out using MINITAB-15 Software. The trial runs specified in L27 orthogonal array were conducted on Lathe Machine for turning operations. 106
4.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Table 2.3: ANOVA Table for Means Parameter DF SS MS F P Depth of Cut 2 11478.63 5739.32 37.96 0.000 Feed 2 13.30 6.7 0.04 0.957 Spindle Speed 2 530.9 265.4 1.76 0.198 Error 20 3023.9 151.2 Total 26 15046.7 Table 2.4: ANOVA Table for Signal-to-Noise Ratios for the Response Data Parameter DF SS MS F P Depth of Cut 2 1734.04 867.02 34.71 0.000 Feed 2 7.16 3.58 0.14 0.867 Spindle Speed 2 84.48 42.24 1.69 0.210 Error 20 499.6 24.98 Total 26 2325.28 Table 2.5: Response Table for Average Surface Roughness Depth of Cut Feed Rate Level Spindle Speed (C) (A) (B) 1 8.157 32.717 30.427 2 31.700 33.737 29.010 3 58.624 32.028 39.044 Delta (∆max-min) 50.468 1.709 10.034 Rank 1 3 2 From Table 2.5, Optimal Parameters for Turning Operation were A1, B3 and C2. Table 2.5 shows the SN Ratio (SNR) of the surface roughness for each level of the factors. The difference of SNR between level 1 and 3 indicates that Depth of Cut contributes the highest effect (∆max-min = 50.468) on the surface roughness followed by Feed Rate (∆max-min = 1.709) and Spindle Speed (∆max-min = 10.034). Therefore the Predicted optimum value of Surface Roughness βp (Surface Roughness) = 32.82 + [8.157-32.82) ]+ [32.028-32.82)] + [29.010-32.82)] = 3.555 Table 2.6: Response Table for Signal-to-Noise ratio of Surface Roughness Depth of Cut Feed Level Spindle Speed (C) (A) (B) 1 -15.85 -27.51 -27.88 2 -29.67 -26.53 -24.29 3 -34.84 -26.33 -28.19 Delta (∆max-min) 18.98 1.18 3.90 Rank 1 3 2 107
5.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME From Table 2.6, Optimal Parameters for Turning Operation were A1, B3 and C2. Table 2.6 shows the SNR of the surface roughness for each level of the factors. The difference of SNR between level 1 and 3 indicates that Depth of Cut contributes the highest effect (∆max-min = 18.98) on the surface roughness followed by Feed Rate (∆max-min = 1.18) and Spindle Speed (∆max-min = 3.90). Therefore the Predicted optimum value of SN Ratio for Turning Operation. ηp (Surface Roughness) = -26.78 + [-15.85-(-26.78)] + [-26.33-(-26.78)] + [-24.29-(-26.78)] = -12.91 3. RESULTS AND DISCUSSION Comparing the F values of ANOVA Table 2.3 and 2.4 of Surface Roughness with the suitable F values of the Factors (F0.05;2;8 = 4.46) and their Interactions (F0.05;4;8 = 3.84) respectively for 95% confidence level respectively show that the Depth of Cut (F = 37.96 and F = 34.71) and was the only significant factor and other two factors Feed (F = 0.04 and F = 0.14) and Spindle Speed (F = 1.76 and F = 1.69) are the factors found to be insignificant. Main Effects Plot for Means Data Means Depth of Cut (mm) Feed Rate (mm/rev) 60 40 Mean of Means 20 0.5 1.0 1.5 0.002 0.011 0.020 Spindle Speed (rpm) 60 40 20 14.91 25.12 40.03 Figure 3.1: Main Effects Plot for Means Main Effects Plot for Means: Fig 3.1 and Fig 3.5 show the effect of the each level of the three parameters on surface roughness for the mean values of measured surface roughness at each level for all the 27 trial runs. 108
6.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME Main Effects Plot for SN ratios Data Means Depth of Cut (mm) Feed Rate (mm/rev) -15 -20 -25 Mean of SN ratios -30 -35 0.5 1.0 1.5 0.002 0.011 0.020 Spindle Speed (rpm) -15 -20 -25 -30 -35 14.91 25.12 40.03 Signal-to-noise: Smaller is better Figure 3.5: Main Effects Plot for SN ratio From Table 2.5, Table 2.6 and Fig 3.1 and Fig 3.5 optimal levels of the parameters for minimum Surface Roughness are first level of Depth of Cut (A1) i.e 0.5 mm, third level of Feed (B3) i.e 0.020 and first level of Spindle Speed i.e 25.12 rpm (C2). So the combination of the factors found in 8th trial in Table 2.2 gives the optimum result. Table 3.1: Results of the Confirmation Tests of the optimal levels of the factors Specimen Trial Depth of Feed Rate Spindle Speed Surface Run Cut (mm) (mm-rev) (rpm) Roughness (µm) 1 8 0.5 3 14.03 3.491 2 8 0.5 3 14.03 3.443 4. SUMMARY AND CONCLUSIONS • Optimization of the surface roughness was done using taguchi method and predictive equation was obtained. A confirmation test was then performed which depicted that the selected parameters and predictive equation were accurate to within the limits of the measurement instrument. • The obtained results can be recommended to get the lowest surface roughness for further research works.In this research work, the material used is mild steel with 0.21% carbon content. The experimentation can also be done for other materials having more hardness to see the effect of parameters on Surface Roughness. • Interactions of the different levels of the factors can be included to see the effect. 5. REFERENCES 1. http://www.mfg.mtu.edu/cyberman/quality/sfinish/index.html 2. http://en.wikipedia.org/wiki/Surface_finish 109
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