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The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
The prediction of surface roughness in finish turning of en 19 steel
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The prediction of surface roughness in finish turning of en 19 steel

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  • 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 392 THE PREDICTION OF SURFACE ROUGHNESS IN FINISH TURNING OF EN-19 STEEL Vivek John 1* , Rahul Davis 2 , Kulan Abel Kandulna 3 , Asian Abhishek Kandulna 4 1 Assistant Professor, Department of Mechanical Engineering, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India 2 Assistant Professor, Department of Mechanical Engineering, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India 3 B.Tech Mechanical Engineering, Department of Mechanical Engineering, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India 4 B.Tech Mechanical Engineering, Department of Mechanical Engineering, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India ABSTRACT Surface roughness, is an admensuration of the pattern of the surface. The peaks and valleys are the indicators to determine whether the surface is rough or smooth. Roughness portrays an extensive role in demonstrating how the object will interface with its environment. The method we have used here was a turning process in which there are basically five distinct specifications i.e pressurized coolant jet, rake angle, spindle speed, feed rate and depth of cut. The Taguchi approach is an adequate channel in which response variable can be optimized by taking fewer experimental runs. The aim of the paper is to obtain an optimal setting of these five distinct turning process parameters by using Carbide P- 30 cutting tool in turning En19 steel having carbon percentage 0.39 as specimen. The Analysis of Variance (ANOVA) and Signal-to-Noise (SN) ratio and were used to analyze the performance. The results illustrate that Spindle speed followed by pressurized coolant jet, rake angle, feed rate and depth of cut was the combination of the optimal levels of factors that affects the surface roughness of the specimen. The results have been cross checked by the confirmation experiments. Keywords: EN-19 steel, turning operation, Taguchi Method INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 3, May - June (2013), pp. 392-399 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 393 1. INTRODUCTION Human tendency is to remain one step ahead from others, engineers take keen interest in inventing new surface finish parameters specific to parts that their organizations manufacture. Thus, out of something akin to pride of ownership, new surface finish parameters are born, in response to the existing parameters that may have done the job satisfactorily. In rapidly changing industries, new applications require surface conditions unlike those that occur in the traditional metalworking fields for which most existing parameters were developed1 .During turning operation, cutting tools remove material from the component to achieve the required shape, dimension and surface roughness (finish). However, wear occurs during the cutting action. Surface roughness can be one of the factors in businesses to gain a competitive edge3 . The aim of the industries is focused on low cost production and high quality products in less time. It is very important now for manufacturers to enhance the efficiency of product and process, keeping the tolerances of stricter part maintained, and thus improving the quality of part. Design of experiments via Taguchi method can be used for attaining high quality at minimum cost. Also the quality obtained by means of the optimization of the product or process is found to be cost effective4 . EN19 is a high quality, high tensile, alloy steel and combines high tensile strength, shock resistance, good ductility and resistance to wear5 . EN19 is most suitable for the manufacture of parts such as heavy-duty axles and shafts, gears, bolts and studs. EN19 is capable of retaining good impact values at low temperatures6 . 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 product7 . Surface roughness in turning has been found to be influenced in by a number of factors such as spindle speed, pressurized coolant jet etc8 . 2. METHODOLOGY In this research work L16 Taguchi orthogonal method has been used in order to study the effect of five different process parameters (spindle speed, pressurized coolant jet, rake angle, feed rate, Depth of cut) on the surface roughness of EN19 steel in turning operations by Carbide P-30 cutting tool and surface roughness was measured in each run in Sparko Engineering Workshop, Allahabad. The length of the work piece was found to be 252 mm. Therefore for the following research, EN19 steel with carbon (0.39%), silicon (0.24), Chromium (1%) and Manganese (0.68%) was chosen for specimen material. Table: 2.1 Details of the Turning Operation Factors Level 1 Level2 Depth of Cut(mm) 0.5 1.0 Feed Rate (mm/rev) 0.16 0.8 Spindle Speed (rpm) 760 1560 Pressurized Coolant Jet (bar) 0.5 1 Rake angles (degrees) 40 70
  • 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 394 In this experiment, the assignment of factors was carried out using MINITAB 15 Software. Using the L16 orthogonal array the trial runs have been conducted on Lathe Machine for turning operations. Table 2.2: Results of Experimental Trial Runs for Turning Operation Trial no. Feed Rate (mm/r ev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (deg) Pressurized Coolant Jet(bar) Surface Rough- ness (µm) SN Ratio 1 0.16 780 0.5 4 0.5 41.5 -32.3610 2 0.16 780 0.5 7 1.0 55.2 -34.8388 3 0.16 780 1.0 4 1.0 41.0 -32.2557 4 0.16 780 1.0 7 0.5 131.0 -42.3454 5 0.16 1560 0.5 4 1.0 25.5 -28.1308 6 0.16 1560 0.5 7 0.5 95.1 -39.5636 7 0.16 1560 1.0 4 0.5 87.2 -38.8103 8 0.16 1560 1.0 7 1.0 47.4 -33.5156 9 0.8 780 0.5 4 1.0 41.0 -32.2557 10 0.8 780 0.5 7 0.5 46.2 -33.2928 11 0.8 780 1.0 4 0.5 25.8 -28.2324 12 0.8 780 1.0 7 1.0 34.8 -30.8316 13 0.8 1560 0.5 4 0.5 127.1 -42.0829 14 0.8 1560 0.5 7 1.0 120.0 -41.5836 15 0.8 1560 1.0 4 1.0 74.3 -37.4198 16 0.8 1560 1.0 7 0.5 102.0 -40.1720 Table 2.3: Analysis of Variance for Surface Roughness Source DF Seq SS Adj SS Adj MS F P Feed Rate (mm/rev) 1 140 140 140 0.08 0.787 Spindle speed (rpm) 1 4294 4294 4294 2.46 0.168 Depth of Cut (mm) 1 4 4 4 0.00 0.963 Rake Angle (degrees) 1 1770 1770 1770 1.01 0.353 Pressurized Coolant Jet (bar) 1 2935 2935 2935 1.68 0.243 Spindle speed (rpm)* Pressurized Coolant Jet (bar) 1 321 321 321 0.18 0.638 Depth of Cut (mm)* Rake Angle (degrees) 1 2 2 2 0.00 0.975 Depth of Cut (mm)* Pressurized Coolant Jet (bar) 1 403 403 403 0.23 0.648 Rake Angle (degrees)* Pressurized Coolant Jet (bar) 1 18 18 18 0.01 0.922 Error 6 10487 10487 1748 Total 15 20374
  • 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 395 Table 2.4: Analysis of Variance for SN Ratio Source DF Seq SS Adq SS Adj MS F P Feed Rate (mm/rev) 1 1.02 1.02 1.02 0.03 0.858 Spindle speed (rpm) 1 75.97 75.97 75.97 2.58 0.159 Depth of Cut (mm) 1 0.02 0.02 0.02 0.00 0.981 Rake Angle (degrees) 1 37.81 37.81 37.81 1.28 0.300 Pressurized Coolant Jet (bar) 1 42.34 42.34 42.34 1.44 0.276 Spindle Speed (rpm)* Pressurized Coolant Jet (bar) 1 12.13 12.13 12.13 0.41 0.545 Depth of Cut (mm)* Rake Angle (degrees) 1 1.16 1.16 1.16 0.04 0.849 Depth of Cut (mm)* Pressurized Coolant Jet (bar) 1 1.59 1.59 1.59 0.05 0.824 Rake Angle (degrees)* Pressurized Coolant Jet (bar) 1 0.63 0.63 0.63 0.02 0.888 Error 6 176.58 176.58 29.43 Total 15 349.25 Table 2.5: Response Table for Signal to Noise Ratio Level 1 Feed Rate (mm/rev) (A) Spindle Speed(rpm) (B) Depth of Cut(mm) (C) Rake Angle(degrees) (D) Pressurized Coolant Jet (bar) (E) 1 -35.23 -37.66 -35.51 -33.94 -37.11 2 -35.73 -33.30 -35.45 -37.02 -33.85 ∆max-min 0.51 4.36 0.07 3.07 3.25 Rank 4 1 5 3 2 Table 2.6: Response Table for Means Level Feed Rate (mm/rev) (A) Spindle speed (rpm) (B) Depth of Cut (mm) (C) Rake Angle (degrees) (D) Pressurized Coolant Jet (bar) (E) 1 65.49 84.82 68.95 57.92 81.99 2 71.40 52.06 67.94 78.96 54.90 ∆max-min 5.91 32.76 1.01 21.04 27.09 Rank 4 1 5 3 2 From Table 2.5 and 2.6, Optimal Parameters for Turning Operation were A1, B2, C2, D1and E2 Signal-to-noise ratio (SN) is utilized to measure the deviation of quality characteristic from the target. In this experiment, the response is the surface roughness which should be maximized, so the desired SNR characteristic is in the category of Larger the better.
  • 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 396 Table 2.5 shows the SNR of the surface roughness for each level of the factors. From Table 2.5 the difference of SN ratio between level 1 and 2 indicates that spindle speed contributes the highest effect (∆max-min=4.36) on the surface roughness followed by pressurized coolant jet (∆max-min = 3.25), rake angle (∆max-min= 3.07), feed rate (∆max- min=0.51) and depth of cut (∆max-min=0.07). Table 2.6 indicates the same result in terms of the difference of Mean between level 1 and 2 indicates that spindle speed contributes the highest effect (∆max-min=32.76) on the surface roughness followed by pressurized coolant jet (∆max-min = 27.09), rake angle (∆max- min = 21.04), feed rate (∆max-min=5.91) and depth of cut (∆max-min=1.01). Therefore the Predicted optimal value of Means of Surface Roughness ηp (Surface Roughness) = 68.44+[65.49-68.44]+[52.06-68.44]+[67.94-68.44]+[57.92-68.44]+[54.9-68.44] = 24.55 Therefore the optimal Predicted value of average Surface roughness for SN Ratio µp (SN Ratio) = -35.48+[-35.23+35.48]+33.30+35.48]+[- 35.45+35.48]+[- 33.94+35.48]+[-33.85+35.48] = -29.85 Thus the optimal predicted value of is µp (Surface Roughness) = 29.85 3. RESULTS AND DISCUSSION Comparing the F values of ANOVA Table 2.3 and 2.4 of Surface Roughness with the suitable F values shows that none of the factor was found to be significant moreover none of the interaction were found to be significant. 0.80.16 80 70 60 50 7801560 1.00.5 74 80 70 60 50 1.00.5 Feed Rate(mm/rev) MeanofMeans Spindlespeed (rpm) Depth of Cut (mm) RakeAngle(degrees) Pressurized Coolant Jet (bar) MainEffectsPlotfor Means Data Means 1.00.5 100 75 50 1.00.5 100 75 50 100 75 50 7801560 100 75 50 74 Spindlespeed(rpm) DepthofCut(mm) RakeAngle(degrees) PressurizedCoolantJet(bar) 1560 780 (rpm) speed Spindle 0.5 1.0 Cut(mm) Depth of 4 7 (degrees) RakeAngle 0.5 1.0 (bar) CoolantJet Pressurized InteractionPlotfor Means Data Means Figure 3.1: Main Effects Plot for Means Figure 3.2: Interaction plot for Means
  • 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 397 0.80.16 -34 -35 -36 -37 -38 7801560 1.00.5 74 -34 -35 -36 -37 -38 1.00.5 Feed Rate(mm/rev) MeanofSNratios Spindlespeed (rpm) Depthof Cut(mm) RakeAngle(degrees) Pressurized CoolantJet(bar) MainEffectsPlotforSNratios DataMeans Signal-to-noise: Smaller is better 1.00.5 -30 -35 -40 1.00.5 -30 -35 -40 -30 -35 -40 7801560 -30 -35 -40 74 Spindlespeed(rpm) DepthofCut(mm) RakeAngle(degrees) PressurizedCoolantJet(bar) 1560 780 (rpm) speed Spindle 0.5 1.0 Cut(mm) Depth of 4 7 (degrees) RakeAngle 0.5 1.0 (bar) CoolantJet Pressurized InteractionPlotforSNratios Data Means Signal-to-noise: Smaller is better Figure 3.3: Main Effects Plot for SN ratio Figure 3.4: Interaction Plots for SN ratio From Table 2.5, Table 2.6 and Fig 3.1 and Fig 3.2, optimal levels of the factors for surface toughness are first level of Feed Rate (0.16 mm/rev), second level of Spindle Speed (1560 rpm), second level of Depth of Cut (1 mm), first level of Rake Angle(40 ) and second level of pressurized Coolant Jet (1 bar). The combination of the optimal levels of the parameters was not found within the trials of Table no 2.2 (L-16 orthogonal array) but the obtained combination of the optimal levels of the factors was verified using the confirmation tests. The results of the confirmation experiments are given as follows: Table 2.7: Results of the Confirmation Tests for the optimal levels of the factors Feed Rate (mm/rev) Spindle Speed (rpm) Depth of Cut (mm) Rake Angle (degree) Pressurized Coolant Jet (bar) Surface Roughness 0.16 1560 1.0 4 1.0 24.55 0.16 1560 1.0 4 1.0 24.49 0.16 1560 1.0 4 1.0 24.41 0.16 1560 1.0 4 1.0 24.33 4. Summary and Conclusions • The optimization of the various levels of the factors followed by confirmation test confirms that the obtained results were found within the limits. • The results attained by the above research work can be suggested to get the minimum surface roughness under the above conditions. • The current research work comprises of the use of EN19 steel having 0.39% carbon. The research work can contain the application of other materials having different chemical compositions. • More number of interactions of the various levels of the factors can also be included in order to expand the research.
  • 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 398 5. REFERENCES 1. Alex Tabenkin is a surface finish and form metrology consultant for Mahr Federal Inc. of Providence, Rhode Island. 2. Raghuwanshi, B. S. (2009). A course in Workshop Technology Vol.II (Machine Tools), Dhanpat Rai & Company Pvt. Ltd. 3. Khorasani, Amir Mahyar et al (2011) Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE), IACSIT International Journal of Engineering and Technology, Vol.3, No.1 4. Ross, Philip J. (2005). Taguchi Techniques for Quality Engineering, Tata McGraw- Hill Publishing Company Ltd. 5. Website: http://www.westyorkssteel.com/en24.html 6. Website: www.kvsteel.co.uk/steel/EN24T.html 7. 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 (ICTMIE'2011) Bangkok 8. 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 27-29 9. 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. 10. Van Luttervelt, C. A. et al (1998). Present situation and future trends in modelling of machining operations, CIRP Ann. 11. Kirby, Daniel (2010). Optimizing the Turning Process toward an Ideal Surface Roughness Target. 12. 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. 13. Kirby, E. Daniel (2006). Optimizing surface finish in a turning operation using the Taguchi parameter design method Int J Adv Manuf Technol: 1021–1029. 14. 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.). 15. 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. 16. Hasegawa. M, et al (1976). Surface roughness model for turning, Tribology International December 285-289. 17. 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. 18. Phadke, Madhav. S. (1989). Quality Engineering using Robust Design. Prentice Hall, Eaglewood Cliffs, New Jersey.
  • 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME 399 19. 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. 20. 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- 144 390-396 21. Palanikumar, K. et al (2006). Assessment of factors influencing surface roughness on the machining of glass –reinforced polymer composites, Journal of Materials and Design. 22. 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. 23. 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. 24. Rahul Davis, “A Parameteric Design Study of Surface Roughness in Dry Turning Operation of En24 Steel”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 410 - 415, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. 25. Rahul Davis and Mohamed Alazhari, “Analysis and Optimization of Surface Roughness in Dry Turning Operation of Mild Steel”, International Journal of Industrial Engineering Research and Development (IJIERD), Volume 3, Issue 2, 2012, pp. 1 - 9, ISSN Online: 0976 - 6979, ISSN Print: 0976 – 6987. 26. Rahul Davis, Joseph Emmanuel, Md. Imroz Alam and Akash Sunny, “Taguchi Method And Anova: An Approach for Process Parameters Optimization of Wet Turning Operation While Turning En 353 Steel”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 4, 2013, pp. 1 - 7, ISSN Print: 0976-6480, ISSN Online: 0976-6499. 27. Rahul Davis and Mohamed Alazhari, “Optimization of Cutting Parameters in Dry Turning Operation of Mild Steel”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 104 - 110, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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