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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
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Turning parameters optimization for surface roughness by taguchi method

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  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME203TURNING PARAMETERS OPTIMIZATION FOR SURFACEROUGHNESS BY TAGUCHI METHODAjeet Kumar rai*, Richa Dubey, Shalini yadav and Vivek SachanMechanical Engineering DepartmentSam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad-211004, IndiaABSTRACTIn the present study an attempt has been made to investigate the effect of cuttingparameters (cutting speed, feed rate, and depth of cut) on surface roughness in a turningoperation of cast iron. Experiments have been conducted using Taguchi’s experimentaldesign technique. An orthogonal array, the signal to noise ratio, and the analysis of varianceare employed to investigate the cutting characteristics of cast iron using carbide tool.Optimum cutting parameters for minimizing surface roughness were determined.Experimental results reveal that among the cutting parameters, the cutting speed is mostsignificant machining parameter for surface roughness followed by feed rate and depth of cutin the specified test range.Keywords:, Optimization, Taguchi method, S/N ratio. Turning operationINTRODUCTIONIn recent years the challenge before the manufacturers is to increase the productionrate, decreasing operation cost, and enhancing the quality of production. Among the severalfactors machining parameters will affect them most [1]. Among these machining parameters,cutting speed, feed rate and depth of cut will play a significant role in machining quality thatare controlled by the user. Therefore, suitable selection of these parameters is necessary toreach optimal machining conditions to enhance production efficiency. Several researchershave performed experimental investigations about the machining operations and evaluatedthe effect of machining parameters on the output of the process. [2, 3]. But implementingnumerous experimental tests for finding optimal conditions of the process is time consumingand costly. In order to find a solution to this problem many researchers have attempted toINTERNATIONAL JOURNAL OF MECHANICAL ENGINEERINGAND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online)Volume 4, Issue 3, May - June (2013), pp. 203-211© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2013): 5.7731 (Calculated by GISI)www.jifactor.comIJMET© I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME204model the machining processes by various methods such as statistic, intelligent and analyticalmethods.[4,5]. Yang et al. [6] have used Taguchi method to optimize the turning operation ofS45C steel bars using tungsten carbide cutting tools and reported that cutting speed, feed rate,and depth of cut are the significant cutting parameters which affect surface roughness.However, the contribution order of the cutting parameters for surface roughness is feed rate,then depth of cut, and then cutting speed. Zhang et al. [7] have used Taguchi method forsurface finish optimization in end milling of Aluminum blocks. The experimental resultsindicate that the effects of spindle speed and feed rate on surface finish were larger than depthof cut for milling operation. Nalbant et al. [8] used Taguchi method to find optimum cuttingparameters for surface roughness in turning of AISI 1030 carbon steel bars using TiN coatedtools. Three cutting parameters namely, insert radius, feed rate, and depth of cut areoptimized with considerations of surface roughness. In turning, use of greater insert radius,low feed rate and low depth of cut are recommended to obtain better surface roughness forthe specific test range. Ghani et al. [9] applied Taguchi method to find optimum cuttingparameters for surface roughness and cutting force in end milling when machining hardenedsteel AISI H13 with TiN coated P10 carbide insert tool under semi-finishing and finishingconditions of high speed cutting. The milling parameters evaluated is cutting speed, feed rate,and depth of cut. In end milling, use of high cutting speed, low feed rate and low depth of cutare recommended to obtain better surface roughness and low cutting force.From the above stated literature review, it becomes clear that the Taguchi Design method hasbeen widely applied with great success for optimizing industrial/production processes.TAGUCHI METHODThe Taguchi approach is a form of DOE with special application principles. For mostexperiments carried out in the industry, the difference between the DOE and Taguchiapproach is in the method of application [10].Fig 1: Scheme of the major steps of Taguchi method [11]Identify thefactors/interactionsIdentify thelevels of eachfactorSelect anappropriateorthogonal array(OA)Assign thefactors/interactions to the columnof the OAConduct theexperimentsAnalyse thedata, Determinethe optimumlevelsConduct theconfirmationexperiment
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME205Taguchi method is a technique for designing and performing experiments toinvestigate processes where the output depends on many factors (variables, inputs)without having tediously and uneconomically run of the process using all possiblecombinations of values. Thanks to systematically chosen certain combinations ofvariables it is possible to separate their individual effects [12]. The tool used in theTaguchi method is the orthogonal array (OA). OA is the matrix of numbers arranged incolumns and rows [13]. The Taguchi method employs a generic signal- to –noise (S/N)ratio to quantify the present variation. These S/N ratios are meant to be used as measuresof the effect of noise factors on performance characteristics. S/N ratios take into accountboth amount of variability in the response data and closeness of the average response totarget. There are several S/N ratios available depending on type of characteristics: smalleris better, nominal is better and larger is better.[6,12]EXPERIMENTAL DESIGNTaguchi method based design of experiment has been used to study the effect ofthree machining process parameters on the output parameter of surface roughness. Table1 shows three factors and three levels used in the experiment. For selecting appropriatearrays, degree of freedom of array is calculated. There are six degrees of freedom owingto three machining parameters, so Taguchi based L27 orthogonal array is selected (Table2). Accordingly 27 experiments were carried out to study the effect of machining inputparameters. Each experiment was repeated three times in order to reduce experimentalerrors.Table 1: Level of process parametersSymbol Factors Level 1 Level 2 Level 3A Cutting Speed (rpm) 780 1560 2340B Feed (mm/rev) 0.4 0.8 0.16C Depth of cut (mm) 0.4 0.5 0.6
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME206Table 2:Taguchi’s L27 orthogonal arrayFactorStandard order A B C1 780 0.4 0.42 780 0.4 0.53 780 0.4 0.64 780 0.8 0.45 780 0.8 0.56 780 0.8 0.67 780 0.16 0.48 780 0.16 0.59 780 0.16 0.610 1560 0.4 0.411 1560 0.4 0.512 1560 0.4 0.613 1560 0.8 0.414 1560 0.8 0.515 1560 0.8 0.616 1560 0.16 0.417 1560 0.16 0.518 1560 0.16 0.619 2340 0.4 0.420 2340 0.4 0.521 2340 0.4 0.622 2340 0.8 0.423 2340 0.8 0.524 2340 0.8 0.625 2340 0.16 0.426 2340 0.16 0.527 2340 0.16 0.6
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME207RESULTS AND DISCUSSIONTwenty-seven experiments were performed using the design parameter combinationsin the specified orthogonal array table. Nine specimens were fabricated for each of theparameter combinations. The complete response table for these data appears in Table 3. Inorder to estimate the effect of factor A (Cutting Speed) on average value of responsevariables, were summed together nine observed response at level 1 of factor A. Then the sumwas divided by nine to obtain the average response. Average responses at level 2 and level 3were obtained in the similar manner. The estimated effects are presented graphically in fig. 2.The range of average responses over the three levels of each experimental factor is:For Cutting speed = 26.314For Feed rate = 10.63889For Depth of cut = 5.8888In particular, factor A, B and C should be set at level 1, level 3 respectively with negligibleeffect of depth of cut parameter.Figure 2: Estimated factor effectsThe sample standard deviation is generally accepted measure of variability instatistical data analysis and experimental design. This statistics is somewhat more difficult tocalculate than the sample range, but it has desirable properties which make its use worth theadded effort [6, 12].The standard deviation was calculated for each tube in five steps. First, y wassubtracted from each measurement in the sample (sample mean), then the square differencesobtained prior were calculated. Next, the squared obtained differences were and was dividedthe sum by the sample size minus one (s2). Finally obtain the square root of s2. The samplevariance is written ass2= ∑(y-y)2/(n-1) (1)s = √s2(2)051015202530354045A1 A2 A3 B1 B2 B3 C1 C2 C3AverageLevel of Factor
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME208Table 3: Experimental data sample statisticsExperimentnumberObserved response values forSurface roughness (µm)Mean StandardDeviationLogarithmOf S.D.S/NRatio1. 6.25 13.75 28.75 36.25 21.25 13.6930 1.1364 -26.5472. 17.5 7.5 7.5 32.5 16.25 11.8145 1.0724 -24.2173. 30 15 10 35 22.5 11.9023 1.07563 -27.0434. 15 0 0 15 7.5 8.660 0.9375 -17.5015. 30 0 5 20 13.75 13.768 1.1389 -22.7666. 22.5 2.5 7.5 12.5 11.25 8.5391 0.93141 -21.0237. 30 5 10 0 11.25 13.1497 1.1189 -21.0238. 10 10 0 5 6.25 4.7871 0.68007 -15.9179. 30 0 15 5 12.5 13.2287 1.1215 -21.93810. 20 15 15 5 13.75 6.2915 0.7987 -22.76611. 58.75 6.25 26.25 26.25 29.375 21.7346 1.3371 -29.35912. 7.5 5 7.5 17.5 9.375 5.5433 0.7437 -19.43913. 3.75 3.75 3.75 11.25 5.625 3.75 0.5740 -15.00214. 36.25 8.75 23.75 3.75 18.125 14.7725 1.1694 -25.16515. 38.75 13.75 3.75 56.25 28.125 23.8375 1.3772 -28.98116. 2.5 27.5 2.5 29.5 15.5 15.0332 1.1770 -23.80617. 20 25 15 20 20 4.0824 0.6109 -26.02018. 35 15 15 0 16.25 14.3614 1.1571 -24.21719. 110 35 110 180 108.75 59.2135 1.7724 -40.72820. 5 20 5 30 15 12.2474 1.0880 -23.52121. 38.75 28.75 11.25 56.25 33.75 18.8193 1.2746 -30.56522. 148.75 51.25 21.25 76.25 49.375 61.6230 1.7897 -33.87023. 47.5 22.5 17.5 52.5 35 17.5544 1.2445 -30.88124. 20 30 5 45 25 16.8325 1.2261 -27.95825. 18.75 3.75 1.25 21.25 11.25 10.2062 1.0088 -21.02326. 61.25 36.25 8.75 88.75 48.75 34.2174 1.5342 -33.75927. 62.5 2.5 37.5 27.5 32.5 24.83277 1.3950 -30.237The estimated log s effects from Table 3 are plotted in Fig.3.In order to minimize thevariability the following optimum results were obtained.Factor A, Cutting Speed at level 2Factor B, feed rate at level 3Factor C, Depth of cut at level 2
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME209Figure 3: Estimated factor effects on log(s)In this work, the minimum surface roughness is the indication of better performance.Therefore, the smaller-is-better for the surface roughness was selected for obtaining optimumresult. The following S/N ratios for the lower-is-better case could be calculated:S/NLB = −10 Log (ଵ௥∑ y௥௜ୀଵ i2)Figure 4: Plot of factor effects on S/N Ratio00.20.40.60.811.21.41.6A1 A2 A3 B1 B2 B3 C1 C2 C3Log(s)Level of Factor-35-30-25-20-15-10-50A1 A2 A3 B1 B2 B3 C1 C2 C3S/NRatioLevel of Factor
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME210Table 4:Overall mean S/N RatioLevel Average S/N Ratio by factor level Overall meanS/N RatioA B C1 -21.9974 -27.1320 -24.696 -25.38072 -23.8620 -24.7944 -25.73423 -30.2828 -24.21587 -25.7116In order to maximize the S/N ratio the following assignments were done: factor A(Cutting speed) – level 1, factor B (Feed rate) – level 3, factor C (Depth of cut) – level 1.Figure 4 shows that factor A have a strong effect on S/N ratio response. Factor B is the nextmost significant. The above analyses of table 3 and table 4 are summarized in table 5. In thattable the levels of key factors which are optimizing the response are listed. Some significantlevels are shown in fig. 2, 3 and 4. Keep in mind that the objective is to minimize theresponse average, minimize log s, and maximize the S/N Ratio.Table 5. Summary of analyses of factor effectsLevel which was optimizedFactor y Log s S/N RatioA 1 2 1B 3 3 3C Not very significant 2 1In this study factor A and B were dominant. For parameter C, reducing log s will have littleeffect on the performance than the S/N ratio. So level 1 is optimized.The final optimizedvalues are:-1) Cutting speed: - Level 1 – 780 rpm.2) Feed rate: - Level 3 - 0.16 mm/rev.3) Depth of cut: - Level 1 - 0.4 mm.CONCLUSIONSIn this study, the Taguchi optimization method was applied to find the optimalprocess parameters, which minimizes the surface roughness during the dry turning of castiron. A Taguchi orthogonal array, the signal to noise(S/N) ratio and the analysis of variance(ANOVA), were used for the optimization of cutting parameters. ANOVA results shows thatcutting speed, feed rate and depth of cut affects the surface roughness by 38.45%, 4.85% and0.72% respectively. In this experiment depth of cut do not have a significant effect on thesurface roughness in the specified test range, however 0.4 mm will be the optimum value.Confirmation experimentation was also conducted and verified the effectiveness of theTaguchi optimization method.
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME211REFERENCES[1] T. Childs, K. Maekawa, T. Obikawa and Y. Yamane, metal cutting theory and application,New York, USA (2000).[2] A. Javidi, U. Riegger and W. Eichlseder, The effect of machining on the surface integrityand fatigue life, International journal of Fatigue, 30 (2008) 2050-2055.[3] D. I. Lalwani, N. K. Mehta and P. K. Jain, Experimental investigations of cuttingparameters influence on cutting forces and surface roughness in finish hard turning ofMDN250 steel, Journal of Materials Processing Technology, 206 (2008) 167-179.[4] I. Mukherjee and P. K. Ray, A review optimizations techniques in metal cutting processes,Computers & Industrial Engineering, 50 (2006) 15-34.[5] C. X. J. Feng and X. Wang, Development of empirical models for surface roughnessprediction in finish turning, international journal of Advanced Manufacturing Technology,20(2002) 348-356.[6] Yang, W.H.; and Tarng, Y.S. (1998), Design optimization of cutting parameters forturning operations based on the Taguchi method. Journal of Material ProcessingTechnology, 84(1-3), 122-129.[7] Zhang, J.Z.; Chen, J.C.; and Kirby, E.D. (2007). Surface roughness optimization in an end-milling operation using the Taguchi design method. Journal of Material ProcessingTechnology, 184(1-3), 233-239.[8] Nalbant, M.; Gokkaya, H.; and Sur, G. (2007). Application of Taguchi method in theoptimization of cutting parameters for surface roughness in turning. Materials & Design,28(4), 1379-1385.[9] Ghani, J.A.; Chodhury, I.A.; and Hassan, H.H. (2004). Application of Taguchi method inthe optimization of end milling parameters. Journal of Material Processing Technology,145(1), 84-92.[10] Roy R. K. (2001). Design of experiments using the Taguchi approach. John Willey &Sons. Inc. New York.[11] Chen Y. H. ,Tam S.C., Chen W. L. Zheng H.Z. (1996). Application of Taguchi method inthe optimization of laser microengraving of photomasks. International journal of materialsand product technology. 11, 333-344[12] Lochner R.H. Matar J.E.,(1990) Design for quality- An introduction to the best of Taguchiand western methods of statistical experimental design. New York.[13] Sharma P, Verma A. Sidhu R.K. Pandey O.P. (2005). Process parameter selection forstrontium ferrite sintered magnets using Taguchi L9orthogonal design, Journal of materialsprocessing technology 168, 147-151.[14] Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar, “Applicationof Taguchi Method and Anova in Optimization of Cutting Parameters for MaterialRemoval Rate and Surface Roughness in Turning Operation”, International Journal ofMechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 47 - 53,ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.[15] Ajeet Kumar Rai, Vivek Sachan and Maheep Kumar, “Experimental Investigation Of ADouble Slope Solar Still With A Latent Heat Storage Medium”, International Journal ofMechanical Engineering & Technology (IJMET), Volume 4, Issue 1, 2013, pp. 22 - 29,ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.[16] Ajeet Kumar Rai and Mustafa S Mahdi, “A Practical Approach to Design andOptimization of Single Phase Liquid to Liquid Shell and Tube Heat Exchanger”,International Journal of Mechanical Engineering & Technology (IJMET), Volume 3,Issue 3, 2013, pp. 378 - 386, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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