International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 649...
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Application of taguchi method in the optimization of boring parameters 2

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Application of taguchi method in the optimization of boring parameters 2

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME191APPLICATION OF TAGUCHI METHOD IN THE OPTIMIZATION OFBORING PARAMETERSAjeet Kumar rai*, Shalini yadav, Richa Dubey and Vivek SachanMechanical Engineering DepartmentSam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad-211004,IndiaABSTRACTIn the present study, Taguchi method is applied to find optimum process parametersin the boring operation of a cast iron work piece. A L27 orthogonal array, signal-to-noise ratioand analysis of variances are applied to study the performance characteristics of machiningparameters (cutting speed, feed rate and depth of cut) with consideration of surface finish.Experimental results reveal that among the cutting parameters, the depth of cut is mostsignificant machining parameter for surface roughness followed by feed rate and cuttingspeed in the specified test range.Keywords:, Optimization, Taguchi method, S/N ratio, Boring operationINTRODUCTIONTaguchi parameter design offers a systematic approach for optimization of variousparameters with regards to performance, quality and cost. And it is important in a sense tomeet the challenge coming before the manufacturers, which are to increase the productionrate, reducing operating cost and enhancing the quality of production. Taguchi primarilyrecommends experimental design as a tool to make products more robust- to make them lesssensitive to noise factors. He views experimental design as a tool for reducing the effect ofvariation on product and process quality characteristics [1]. The complete procedure inTaguchi design method can be divided into three stages: system design, parameter design andtolerance design. Of the three design stages, the second stage- the parameter design –isconsidered to be the most important stage [2]. This stage of Taguchi parameter designrequires that the factors affecting quality characteristics in the manufacturing process have tobe determined. The major goal of this stage is to identify the optimal cutting conductions thatINTERNATIONAL JOURNAL OF ADVANCED RESEARCH INENGINEERING AND TECHNOLOGY (IJARET)ISSN 0976 - 6480 (Print)ISSN 0976 - 6499 (Online)Volume 4, Issue 4, May – June 2013, pp. 191-199© IAEME: www.iaeme.com/ijaret.aspJournal 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), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME192yield the lowest surface roughness value. Few steps to be followed in the Taguchi parameterdesign are: selecting the proper orthogonal array (OA) according to the numbers ofcontrollable factors, running experiments based on the OA, analyzing data, Identifying theoptimum condition, and conducting confirmation runs with the optimal levels of all theparameters. Taguchi method is used by several researchers to carry out their studies invarious machining operations like turning, end milling, drilling etc.Yang et al [3] used the Taguchi parameter design in order to identify optimum surfaceroughness performance on an aluminum material with cutting parameters of depth of cut,cutting speed, feed rate and tool diameter. It was found that tool diameter is not a significantcutting factor affecting the surface roughness. Bagci et al [4] used the Taguchi method toexplore the effects of drilling parameters on the twist drill bit temperature for a designoptimization of cutting parameters. Zhang et al [5] performed a study of the Taguchi Designapplication to optimize surface quality in a CNC face milling operation. Taguchi design wassuccessful in optimizing milling parameters for surface roughness. Nalbant et al. [6] usedTaguchi method to find optimum cutting parameters for surface roughness in turning of AISI1030 carbon steel bars using TiN coated tools. Three cutting parameters namely, insertradius, feed rate, and depth of cut are optimized with considerations of surface roughness. Inturning, use of greater insert radius, low feed rate and low depth of cut are recommended toobtain better surface roughness for the specific test range. Ghani et al. [7] applied Taguchimethod to find optimum cutting parameters for surface roughness and cutting force in endmilling when machining hardened steel AISI H13 with TiN coated P10 carbide insert toolunder semi-finishing and finishing conditions of high speed cutting. The milling parametersevaluated is cutting speed, feed rate, and depth of cut. In end milling, use of high cuttingspeed, low feed rate and low depth of cut are recommended to obtain better surface roughnessand low cutting force. Kurt et al [6] employed the Taguchi method in the optimization ofcutting parameters for surface finish and hole diameter accuracy in dry drilling processes.The validity of the Taguchi approach to process optimization was well established.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.Keeping this perspective the present work has been taken with the objective to investigate theeffects of different boring parameters on surface roughness, and is to determine the optimalboring parameters using the Taguchi technique.EXPERIMENTAL DESIGNTable1 shows three factors and three levels used in the experiment. For selectingappropriate arrays, degree of freedom of array is calculated. There are six degrees of freedomowing to three machining parameters, so Taguchi based L27 orthogonal array is selected(Table 2). Accordingly 27 experiments were carried out to study the effect of machininginput parameters. 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 (m/min) 80 100 120B Feed (mm/rev.) 0.05 0.1 0.15C Depth of cut (mm) 0.3 0.4 0.5
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME193Table 2: Taguchi’s L27 orthogonal arrayFactorStandard order A B C1 80 0.05 0.32 80 0.05 0.43 80 0.05 0.54 80 0.1 0.35 80 0.1 0.46 80 0.1 0.57 80 0.15 0.38 80 0.15 0.49 80 0.15 0.510 100 0.05 0.311 100 0.05 0.412 100 0.05 0.513 100 0.1 0.314 100 0.1 0.415 100 0.1 0.516 100 0.15 0.317 100 0.15 0.418 100 0.15 0.519 120 0.05 0.320 120 0.05 0.421 120 0.05 0.522 120 0.1 0.323 120 0.1 0.424 120 0.1 0.525 120 0.15 0.326 120 0.15 0.427 120 0.15 0.5RESULTS AND DISCUSSIONThe Taguchi method employs a generic signal- to–noise (S/N) ratio to quantify thepresent variation. These S/N ratios are meant to be used as measures of the effect of noisefactors on performance characteristics. S/N ratios take into account both amount of variabilityin the response data and closeness of the average response to target. There are several S/Nratios available depending on type of characteristics: smaller is better, nominal is better andlarger is better. Twenty-seven experiments were performed using the design parameter
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME194combinations in the specified orthogonal array table. Nine specimens were fabricated foreach of the parameter combinations. The complete response table for these data appears inTable 3. In order to estimate the effect of factor A (Cutting Speed) on average value ofresponse variables, were summed together nine observed response at level 1 of factor A.Then the sum was divided by nine to obtain the average response. Average responses at level2 and level 3 were obtained in the similar manner. The estimated effects are presentedgraphically in fig. 2. The range of average responses over the three levels of eachexperimental factor is:For Cutting speed = 72.5For Feed rate = 41.6666For Depth of cut = 31.3889In particular, factor A, B and C should be set at level 2, level 3 and level 1 respectively.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.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)050100150200250300350A1 A2 A3 B1 B2 B3 C1 C2 C3AverageLevel of factor
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME195Table 3: Experimental data sample statisticsExperimentNumberObserved responsevalues of SurfaceRoughness (µm)Mean StandardDeviationLog ofS.D.S/N Ratio1 10 15 12.5 3.5355 0.5484 -21.93822 280 300 290 14.1421 1.1505 -49.24793 340 360 350 14.1421 1.1505 -50.88134 400 425 412.5 17.6776 1.2474 -52.30845 450 425 437.5 17.6776 1.2474 -52.81956 440 415 427.5 17.6776 1.2474 -52.61877 345 375 360 21.2132 1.3266 -51.12608 290 320 305 21.2132 1.3266 -49.6859 300 335 317.5 24.7487 1.3935 -50.034810 320 340 330 14.1421 1.1505 -50.370211 335 350 342.5 10.6066 1.0255 -50.693212 300 315 307.5 10.6066 1.0255 -49.75613 230 245 237.5 10.6066 1.0255 -47.513214 145 165 155 14.1421 1.1505 -43.806615 250 225 237.5 17.6776 1.2474 -47.513216 240 220 230 14.1421 1.1505 -47.234517 280 305 292.5 17.6776 1.2474 -49.322518 115 140 127.5 17.6776 1.2474 -42.110219 275 285 280 7.0710 0.8494 -48.943120 210 225 217.5 10.6066 1.0255 -46.749121 290 300 295 7.0710 0.8494 -49.396422 250 280 265 21.2132 1.3266 -48.464923 290 315 302.52 17.6776 1.2474 -49.614524 275 250 262.5 17.6776 1.2474 -48.382525 230 210 220 14.1421 1.1505 -46.848426 275 300 287.5 17.6776 1.2474 -49.172727 215 230 222.5 10.6066 1.0255 -46.9466The 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 3Factor B, feed rate at level 1Factor C, Depth of cut at level 1
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME196Figure 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 = −10Log (ଵ௥∑ y௥௜ୀଵ i2)Fig 4 photograph showing experimentation00.20.40.60.811.21.4A1 A2 A3 B1 B2 B3 C1 C2 C3Log(s)Level of Factor
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME197Figure 4: Plot of factor effects on S/N RatioTable 4: Overall mean S/N RatioLevel Average S/N Ratio by factor level Overall meanS/N RatioA B C1 -47.8510 -46.4417 -46.0829 -47.90722 -47.5910 -49.2268 -49.01233 -48.2798 -48.0534 -48.6266In order to maximize the S/N ratio the following assignments were done: factor A(Cutting speed) – level 2, factor B (Feed rate) – level 1, factor C (Depth of cut) – level 1.Figure 4 shows that factor C 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 2 3 2B 3 1 1C 1 1 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 2 – 100 m/min.2) Feed rate: - Level 1 - 0.15 mm/rev.3) Depth of cut: - Level 1 - 0.3 mm.-49.5-49-48.5-48-47.5-47-46.5-46-45.5-45-44.5A1 A2 A3 B1 B2 B3 C1 C2 C3S/NRatioLevel of Factor
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME198CONCLUSIONSIn this study, the Taguchi optimization method was applied to find the optimalprocess parameters, which minimizes the surface roughness during the boring of cast iron. ATaguchi orthogonal array, the signal to noise(S/N) ratio and the analysis of variance(ANOVA), were used for the optimization of cutting parameters. Results show that depth ofcut will have great influence on the surface roughness followed by feed and cutting speed.REFERENCES[1] ] Lochner R.H. Matar J.E.,(1990) Design for quality- An introduction to the best ofTaguchi and western methods of statistical experimental design. New York.[2] Taguchi G., Sayed M.Ei., and Hsaing C., (1989) Quality engineering and quality systems.McGraw-Hill NY.[3]Yang , J. L., Chen J.C. (2001) A systematic approach for identifying optimum surfaceroughness performance in end-milling operations. Journal of Industrial Technology, vol 17,No 2, P1-8.[4] Bagci E., Ozcelik B. (2006) Analysis of temperature changes on the twist drill underdifferent drilling conditions based on Taguchi method during dry drilling of AI 7075-T65.International JOUrnal of Advanced manufacturing Technology, vol 29,no 7-8, p 629-636.[5] Zhang, J.Z.; Chen, J.C.; and Kirby, E.D. (2007). Surface roughness optimization in anend-milling operation using the Taguchi design method. Journal of Material ProcessingTechnology, 184(1-3), 233-239.[6] 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.[7] 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.[8] Kurt M, Bagci E., Kaynak Y., (2009) Application of Taguchi methods in the optimizationof cutting parameters for surface finish and hole diameter accuracy in dry drilling processes.T. Childs, K. Maekawa, T. Obikawa and Y. Yamane, metal cutting theory and application,New York, USA (2000).[9] 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.[10] Ajeet Kumar Rai and Ashish Kumar, “A Review on Phase Change Materials & TheirApplications”, International Journal of Advanced Research in Engineering & Technology(IJARET), Volume 3, Issue 2, 2012, pp. 214 - 225, ISSN Print: 0976-6480, ISSN Online:0976-6499[11] Ajeet Kumar Rai, Richa Dubey, Shalini Yadav and Vivek Sachan, “Turning ParametersOptimization for Surface Roughness by Taguchi Method”, International Journal ofMechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 203 - 211,ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME199APPENDIXFig A photograph showing machined parts

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