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Optimization of cutting parameters in dry turning operation of mild steel
 

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 Optimization of cutting parameters in dry turning operation of mild steel Document Transcript

    • 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), © IAEMEISSN 0976 - 6480 (Print) IJARETISSN 0976 - 6499 (Online)Volume 3, Issue 2, July-December (2012), pp. 104-110© IAEME: www.iaeme.com/ijaret.html ©IAEMEJournal 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
    • 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), © IAEMEUnfortunately, most manufacturing processes produce parts with surfaces that are eitherunsatisfactory from the standpoint of geometrical perfection or quality of surface texture.This primer begins by explaining how industry controls and measures the precise degree ofsmoothness and roughness of a finished surface.1 Mild steel has a relatively low tensile strength, but it is cheap and malleable, surfacehardness can be increased through carburizing. Carbon content makes mild steel malleableand ductile, but it cannot be hardened by heat treatment2. Since Turning is the primaryoperation in most of the production process in the industry, surface finish of turnedcomponents has greater influence on the quality of the product3. Surface finish in turning hasbeen 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 fluidsetc4. There are three primary input control parameters in the basic turning operations. Theyare feed, spindle speed and depth of cut. Feed is the rate at which the tool advances along itscutting path. Speed always refers to the spindle and the work piece. Depth of cut is thethickness 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 thestudy the effect of three different parameters (depth of cut, feed & spindle speed) on thesurface roughness of the specimens of mild steel was aimed after turning operations weredone 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 workpiecefixture and the measurements of surface roughness were taken across the lay, while the setupwas a three-jaw chuck in Sparko Engineering Workshop, Allahabad (U.P.) India. The totallength of the workpiece (152.4 mm) was divided into 6 equal parts and the surface roughnessmeasurements were taken of each 25.4 mm around each workpiece.The turning operations were performed by high speed steel cutting tool in dry cuttingcondition.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 OperationFactors Level 1 Level 2 Level 3Depth of cut (mm) 0.5 1.0 1.5Feed Rate (mm/rev) 0.002 0.011 0.020Spindle Speed (rpm) 14.91 25.12 40.03 105
    • 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), © IAEMETable 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
    • 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), © IAEMETable 2.3: ANOVA Table for MeansParameter DF SS MS F PDepth of Cut 2 11478.63 5739.32 37.96 0.000Feed 2 13.30 6.7 0.04 0.957Spindle Speed 2 530.9 265.4 1.76 0.198Error 20 3023.9 151.2Total 26 15046.7Table 2.4: ANOVA Table for Signal-to-Noise Ratios for the Response DataParameter DF SS MS F PDepth of Cut 2 1734.04 867.02 34.71 0.000Feed 2 7.16 3.58 0.14 0.867Spindle Speed 2 84.48 42.24 1.69 0.210Error 20 499.6 24.98Total 26 2325.28Table 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 2From 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 thehighest 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 Roughnessβp (Surface Roughness)= 32.82 + [8.157-32.82) ]+ [32.028-32.82)] + [29.010-32.82)] = 3.555Table 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
    • 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), © IAEMEFrom 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. Thedifference of SNR between level 1 and 3 indicates that Depth of Cut contributes the highesteffect (∆max-min = 18.98) on the surface roughness followed by Feed Rate (∆max-min = 1.18) andSpindle 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)]= -12.913. RESULTS AND DISCUSSION Comparing the F values of ANOVA Table 2.3 and 2.4 of Surface Roughness with thesuitable 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 andF = 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 MeansMain Effects Plot for Means: Fig 3.1 and Fig 3.5 show the effect of the each level of thethree parameters on surface roughness for the mean values of measured surface roughness ateach level for all the 27 trial runs. 108
    • 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 3.4434. 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
    • 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), © IAEME3. http://en.wikipedia.org/wiki/Carbon_steel4. Diwakar Reddy.V, Krishnaiah.G. et al (2011), ANN Based Prediction of Surface Roughness in Turning, International Conference on Trends in Mechanical and Industrial Engineering (ICTMIE2011) Bangkok5. Mahapatra, S.S. et al (2006). Parametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method, Proceedings of the International Conference on Global Manufacturing and Innovation - July 27-296. Raghuwanshi, B. S. (2009). A course in Workshop Technology Vol.II (Machine Tools), Dhanpat Rai & Company Pvt. Ltd.7. Ross, Philip J. (2005). Taguchi Techniques for Quality Engineering, Tata McGraw-Hill Publishing Company Ltd.8. Suhail, Adeel H. et al (2010). Optimization of Cutting Parameters Based on Surface Roughness and Assistance of Workpiece Surface Temperature in Turning Process, American J. of Engineering and Applied Sciences 3 (1): 102-108.9. Van Luttervelt, C. A. et al (1998). Present situation and future trends in modelling of machining operations, CIRP Ann.10. Kirby, Daniel (2010). Optimizing the Turning Process toward an Ideal Surface Roughness Target.11. Selvaraj, D. Philip et al (2010). optimization of surface roughness of aisi 304 austenitic stainless steel in dry turning operation using Taguchi design method Journal of Engineering Science and Technology,Vol. 5, no. 3 293 – 301, © school of engineering, Taylor’s university college.12. Kirby, E. Daniel (2006). Optimizing surface finish in a turning operation using the Taguchi parameter design method Int J Adv Manuf Technol: 1021–1029.13. Tzou, Guey-Jiuh and Chen Ding-Yeng (2006). Application of Taguchi method in the optimization of cutting parameters for turning operations. Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taiwan, (R.O.C.).14. Singh, Hari (2008). Optimizing Tool Life of Carbide Inserts for Turned Parts using Taguchi’s design of Experiments Approach, Proceedings of the International MultiConference of Engineers and Computer Scientists Vol II IMECS 2008, 19-21 March, Hong Kong.15. Hasegawa. M, et al (1976). Surface roughness model for turning, Tribology International December 285-289.16. Kandananond, Karin (2009). Characterization of FDB Sleeve Surface Roughness Using the Taguchi Approach, European Journal of Scientific Research ISSN 1450-216X Vol.33 No.2 , pp.330-337 © EuroJournals Publishing, Inc.17. Phadke, Madhav. S. (1989). Quality Engineering using Robust Design. Prentice Hall, Eaglewood Cliffs, New Jersey.18. Aruna, M. (2010). Wear Analysis of Ceramic Cutting Tools in Finish Turning of Inconel 718. International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4253-4262.19. Arbizu, Puertas. I. and Luis Prez, C.J. (2003). Surface roughness prediction by factorial design of experiments in turning processes, Journal of Materials Processing Technology, 143- 144 390-39620. Palanikumar, K. et al (2006). Assessment of factors influencing surface roughness on the machining of glass –reinforced polymer composites, Journal of Materials and Design.21. Sundaram, R.M., and Lambert, B.K. (1981). Mathematical models to predict surface finish in fine turning of steel, Part II, International Journal of Production Research.22. Thamizhmanii, S., et al (2006). Analyses of roughness, forces and wear in turning gray cast iron, Journal of achievement in Materials and Manufacturing Engineering, 17.23. Thamizhmanii, S., et al (2006). Analyses of surface roughness by turning process using Taguchi method, journal of Achievements in Materials and Manufacturing Engineering. Received 03.11.2006; accepted in revised form 15.11.2006.24. Yang, W.H. and Y.S. Tarng (1998), Design optimization of cutting parameters for turning operations based on the Taguchi method. Journal of Materials Processing Technology. 110