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Taguchi method and anova an approach for process parameters 2


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Taguchi method and anova an approach for process parameters 2

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME1TAGUCHI METHOD AND ANOVA: AN APPROACH FOR PROCESSPARAMETERS OPTIMIZATION OF WET TURNING OPERATIONWHILE TURNING EN 353 STEELRahul Davis 1*1Assistant Professor, Department of Mechanical Engineering,SSET, SHIATS, Allahabad -211007, Uttar Pradesh, IndiaJoseph Emmanuel 22Assistant Professor, Department of Mechanical Engineering,SSET, SHIATS, Allahabad -211007, Uttar Pradesh, IndiaMd. Imroz Alam 33B.Tech Mechanical Engineering, Department of Mechanical Engineering,SSET, SHIATS, Allahabad -211007,Uttar Pradesh, IndiaAkash Sunny 44B.Tech Mechanical Engineering, Department of Mechanical Engineering,SSET, SHIATS, Allahabad -211007Uttar Pradesh, IndiaABSTRACTIn the Modern world the quality of the surface finish is one of the most importantrequirements for many turned work pieces due to which manufacturers are seeking to remaincompetitive in the market. Taguchi Parameter Design is a powerful tool and efficient methodfor optimizing quality and performance output of manufacturing processes. This paperfocuses on the use of Taguchi Parameter Design for optimizing surface roughness generatedby a turning operation of EN-353 steel (0.14% C) with Hardness 25±2 by a P-30 carbidecutting tool. This study utilizes a standard orthogonal array for determining the optimumturning parameters, with an applied noise factor. Controlled factors include spindle speed,feed rate, and depth of cut, rake angle, Pressurised Coolant. The results obtained by thismethod will be useful to other research works for similar type of study for further research ontool vibrations, cutting forces etcINTERNATIONAL JOURNAL OF ADVANCED RESEARCH INENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online)Volume 4, Issue 4, May – June 2013, pp. 01-07© IAEME: Impact Factor (2013): 5.8376 (Calculated by GISI)www.jifactor.comIJARET© 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 4, Issue 3, May – June (2013), © IAEME2Keywords: EN353 steel, Wet turning, Surface Roughness, Taguchi Method1. INTRODUCTIONAchieving a desired level of surface quality for turned parts requires practicalknowledge and skill to properly set up this type of operation with the given specifications andconditions. A manufacturing engineer or machine setup technician is often expected to utilizeexperience and published shop guidelines for determining the proper machining parametersto achieve a specified level of surface roughness. EN 353 is an alloy case hardening steel issourced from some of the most reputed steel manufactures in the industry. These products aremade of high grade raw material that ensures their durability, high tensile strength andcorrosion resistance, shock resistance, good ductility and resistant to wear. EN 353 is widelyused in machining components such as gears, gear shafts, axle, crown wheels, pinion andmany other applications. Since turning is the primary operation in most of the productionprocess in the industry, surface finish of turned components has greater influence on thequality of the product.Surface finish in turning has been found to be influenced in varyingamounts 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 etc. The three primary processparameters in any basic Turning operation are speed, feed, and depth of cut. Speed alwaysrefers to the spindle and the work piece. Feed is the rate at which the tool advances along itscutting path. Depth of cut is the thickness of the material that is removed by one pass of thecutting tool over the workpiece. After experimentally turning sample workpieces using theselected orthogonal array and parameters, this study produced a verified combination ofcontrolled factors and a predictive equation for determining surface roughness with a givenset of parameters.2. MATERIALS AND METHODSIn the current research work, in order to study the effect of five different parameters(Depth of cut, Feed, Spindle Speed, Rake Angle & Pressurized Coolant Jet) on the SurfaceRoughness of the turned specimens of EN353 using L16 Taguchi orthogonal design, theTurning Operations have been done 16 times followed by measurements of surface roughnessof the workpieces in Sparko Engineering Workshop, Allahabad. The specimen workpiecewere turned by Carbide cutting tool in wet cutting condition.In proposed work, EN353 steel with carbon (0.14%), Nickel (1.5 %), Chromium (1 %) andMolybdenum (0.23 %) was selected for specimen material.The values of the input process parameters for the Turning Operation are as under:Table: 2.1 Control Input Factors and their levelsFactors Level 1 Level 2Depth of cut (mm) 0.5 1.0Feed (mm/rev) 0.16 0.80Spindle Speed (rpm) 780 1560Rake Angle (degree) 4070Pressurized Coolant Jet (bar) 0.5 1.0
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME3In this experiment, the assignment of factors was carried out using MINITAB 15 Software.Using the L16 orthogonal array the trial runs of turning operations have been conducted onLathe Machine.Table 2.2: Results of Experimental Trial Runs for Turning OperationTrialNo.Depthof Cut(mm)FeedRate(mm/rev)SpindleSpeed(rpm)RakeAngle(degree)PressurizedCoolant Jet(bar)SurfaceRoughness(µm)SN Ratio01 0.5 0.16 780 400.5 73.33 -37.305602 0.5 0.16 780 701.0 103.45 -40.294603 1.0 0.16 780 401.0 74.09 -37.395204 1.0 0.16 780 700.5 105.00 -40.423805 0.5 0.16 1560 401.0 116.66 -41.338406 0.5 0.16 1560 700.5 181.66 -45.185207 1.0 0.16 1560 400.5 90.00 -39.084908 1.0 0.16 1560 701.0 45.88 -33.232509 0.5 0.80 780 401.0 83.33 -38.416010 0.5 0.80 780 700.5 149.08 -43.468411 1.0 0.80 780 400.5 43.34 -32.737812 1.0 0.80 780 701.0 57.49 -35.191813 0.5 0.80 1560 400.5 42.75 -32.618714 0.5 0.80 1560 701.0 110.00 -40.827915 1.0 0.80 1560 401.0 36.66 -31.283816 1.0 0.80 1560 700.5 160.00 -44.0824The specimens were turned on the lathe machine in accordance with the experimentaldesign and surface roughness was measured around the part with the workpiece fixture andthe measurements were taken across the lay, while the setup is a three-jaw chuck.The full length of the specimen (250 mm) was divided into 6 equal parts and the surfaceroughness measurements were taken of each 41.6 mm around each workpiece.Table 2.3: ANOVA Table for MeansParameters DF SS MS F PDepth of Cut 1 3838 3838 2.90 0.119Feed Rate 1 721 721 0.54 0.477Spindle Speed 1 558 558 0.42 0.531Rake Angle 1 7762 7762 5.86 0.036Pressurized Coolant Jet 1 2959 2959 2.24 0.166Error 10 13237 1324Total 15 29076
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME4Table 2.4: ANOVA Table for Signal to Noise RatiosParameters DF SS MS F PDepth of Cut 1 42.32 42.32 3.03 0.113Feed Rate 1 15.28 15.28 1.09 0.321Spindle Speed 1 0.37 0.37 0.03 0.875Rake Angle 1 66.12 66.12 4.73 0.055Pressurized Coolant Jet 1 17.91 17.91 1.28 0.284Error 10 139.89 13.99Total 15 281.88Table 2.5: Response Table for Signal-to-Noise ratio of Surface RoughnessLevel Depth ofCut (A)Feed Rate(B)Spindle Speed(C)RakeAngle (D)PressurizedCoolant Jet (E)1 -39.93 -39.28 -38.15 -40.34 -39.362 -36.68 -37.33 -38.46 -36.27 -37.25Delta( max - min )3.25 1.95 0.30 4.07 2.12Rank 2 4 5 1 3From Table 2.5, Optimal levels of the Parameters for Turning Operation were A2, B2, C1, D2& E2Signal-to-noise ratio (SNR) is utilized to measure the deviation of qualitycharacteristic from the target. In this experiment, the response is the surface roughness whichshould be minimized, so the desired SNR characteristic is in the category of smaller thebetter.Table 2.5 shows the SNR of the surface roughness for each level of the factors. Thedifference of SNR between level 1 and 3 indicates that rake angle contributes the highesteffect (∆max-min = 4.07) on the surface roughness followed by depth of cut (∆max-min = 3.25),pressurized coolant jet (∆max-min = 2.12), feed Rate (∆max-min = 1.95) and spindle speed (∆max-min = 0.30).Therefore the Predicted value of S/N Ratio for surface roughnessηp (Surface Roughness)= -38.305+ [(-36.68-(-38.305)] + [-37.33-(-38.305)] + [-38.15-(-38.305)] + [-36.27-(-38.305)]+ [-37.25-(-38.305)]= -32.46
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME53. RESULTS AND DISCUSSIONComparison of the F values of ANOVA Table 2.3 of Surface Roughness with thesuitable F values of the Factors (F0.05;1;10 = 4.96) respectively shows that the all the factorsexcept rake angle were found to be insignificant.Figure 3.1: Main Effects Plot for MeansFigure 3.2: Main Effects Plot for S/N ratiosTable No. 2.5 shows the results of Signal-to-Noise ratio for Surface Roughness.From Table 2.5, Fig 3.1 and Fig 3.2, optimal levels of the parameters for Surface Roughnessare second level of Depth of Cut (1.0 mm), second level of Feed Rate (0.8 mm/rev) and firstlevel of Spindle Speed (780 rpm), second level of Rake angle (70) and second level ofPressurised Coolant Jet (1.0 bar).So the optimal combination of the factors is found in 12thtrial in Table No.2.2 gives theoptimum result.
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME6Table 2.6: Results for Confirmation Test Trial Runs of Turning Operation for SurfaceRoughness for the Combination of Optimal Levels of FactorsSpecimen Depthof Cut(mm)FeedRate(mm-rev)SpindleSpeed(rpm)RakeAngle(degrees)PressurizedCoolantJet(bar)SurfaceRoughness(µm)1 1.0 0.8 780 701.0 61.081 1.0 0.8 780 701.0 61.031 1.0 0.8 780 701.0 60.984. SUMMARY AND CONCLUSIONS• A parameter designs yielded the optimum condition of the controlled parameters, as wellas a predictive equation in each case and comparative study was done. A confirmationtests was then performed which indicated that the selected parameters and predictiveequation were accurate to within the limits of the measurement instrument.• Therefore the above results can be recommended to get the lowest surface roughness forfurther studies.• In the current research work, the material used is EN353 with 0.14% carbon. Theexperimental work can also be done for other materials having more hardness to see theeffect of parameters on Surface Roughness.• In each case interactions of the different levels of the factors can be included and studycan be extended.• The experimental work and research can be extended by different tool geometry,different types of coolants etc. as factors.5. REFERENCES1. Website: Website: Diwakar Reddy.V, Krishnaiah.G, A. Hemanth Kumar and Sushil Kumar Priya(2011),ANN Based Prediction of Surface Roughness in Turning, International Conference onTrends in Mechanical and Industrial Engineering (ICTMIE2011) Bangkok4. S.S.Mahapatra, Amar Patnaik, Prabina Ku. Patnaik (2006), Parametric Analysis andOptimization of Cutting Parameters for Turning Operations based on Taguchi Method,Proceedings of the International Conference on Global Manufacturing and Innovation -July 27-295. B. S. Raghuwanshi (2009), A course in Workshop Technology Vol.II (Machine Tools),Dhanpat Rai & Company Pvt. Ltd.6. Adeel H. Suhail, N. Ismail, S. V. Wong and N. A. Abdul Jalil (2010), Optimization ofCutting Parameters Based on Surface Roughness and Assistance of Workpiece SurfaceTemperature in Turning Process, American J. of Engineering and Applied Sciences 3 (1):102-108.7. C. A. van Luttervelt, T. H. C. Childs (1998), Present situation and future trends inmodelling of machining operations, CIRP Ann.
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 3, May – June (2013), © IAEME78. Daniel Kirby (2010), Optimizing the Turning Process toward an Ideal Surface RoughnessTarget.9. D. Philip Selvaraj, P. Chandramohan (2010), optimization of surface roughness of aisi 304austenitic stainless steel in dry turning operation using Taguchi design method Journal ofEngineering Science and Technology,Vol. 5, no. 3 293 – 301, © school of engineering,Taylor’s university college.10. E. Daniel Kirby (2006), Optimizing surface finish in a turning operation using the Taguchiparameter design method Int J Adv Manuf Technol: 1021–1029.11. Guey-Jiuh Tzou, Ding-Yeng Chen (2006), Application of Taguchi method in theoptimization of cutting parameters for turning operations. Department of MechanicalEngineering, Lunghwa University of Science and Technology, Taiwan, (R.O.C.).12. Hari Singh (2008), Optimizing Tool Life of Carbide Inserts for Turned Parts using Taguchi’sdesign of Experiments Approach, Proceedings of the International MultiConference ofEngineers and Computer Scientists Vol II IMECS 2008, 19-21 March, , Hong Kong.13. Hasegawa. M, A. Seireg, R.A. Lindberg (1976), Surface roughness model for turning,Tribology International December 285-289.14. Karin Kandananond (2009), Characterization of FDB Sleeve Surface Roughness Using theTaguchi Approach, European Journal of Scientific Research ISSN 1450-216X Vol.33 No.2 ,pp.330-337 © EuroJournals Publishing, Inc.15. Madhav. S. Phadke (1989), Quality Engineering using Robust Design. Prentice Hall,Eaglewood Cliffs, New Jersey.16. M. Aruna. Wear Analysis of Ceramic Cutting Tools in Finish Turning of Inconel 718.International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4253-4262.17. Puertas. I. Arbizu, C.J. Luis Prez (2003), Surface roughness prediction by factorial design ofexperiments in turning processes, Journal of Materials Processing Technology, 143- 144390-39618. Palanikumar, L. Karunamoorthy, R. Krathikeyan (2006), Assessment of factors influencingsurface roughness on the machining of glass –reinforced polymer composites, Journal ofMaterials and Design.19. Sundaram. R.M., B.K. Lambert (1981), Mathematical models to predict surface finish in fineturning of steel, Part II, International Journal of Production Research.20. S. Thamizhmanii, S. Saparudin, S. Hasan (2006), Analyses of roughness, forces and wear inturning gray cast iron, Journal of achievement in Materials and Manufacturing Engineering,17,.21. S. Thamizhmanii, S. Saparudin, S. Hasan (2006), Analyses of surface roughness by turningprocess using Taguchi method, journal of Achievements in Materials and ManufacturingEngineering. Received 03.11.2006; accepted in revised form 15.11.2006.22. Philip j. Ross (2005), Taguchi Techniques for Quality Engineering, Tata McGraw-HillPublishing Company Ltd.23. W.H. Yang, Y.S. Tarng (1998), Design optimization of cutting parameters for turningoperations based on the Taguchi method. Journal of Materials Processing Technology24. Kirankumar Ramakantrao Jagtap, S.B.Ubale and Dr.M.S.Kadam, “Optimization ofCylindrical Grinding Process Parameters for AISI 1040 Steel Using Taguchi Method”,International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 1,2012, pp. 226 - 234, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.25. U. D. Gulhane, A. B. Dixit, P. V. Bane and G. S. Salvi, “Optimization of Process Parametersfor 316L Stainless Steel Using Taguchi Method and Anova”, International Journal ofMechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 67 - 72,ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.