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Investigation of turning process to improve productivity mrr for better surface finish of al

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  • 1. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME59INVESTIGATION OF TURNING PROCESS TO IMPROVEPRODUCTIVITY (MRR) FOR BETTER SURFACE FINISH OF AL-7075-T6 USING DOEU. D. Gulhane*, S. P. Ayare, V.S.Chandorkar, M .M . JadhavDepartment of Mechanical Engineering, Finolex Academy of Management and Technology,Ratnagiri, Maharashtra 415612, India*Corresponding author- Associate Professor, Dept. of Mechanical Engineering,Finolex Academy of Management and Technology, P-60/61, MIDC, Mirjole Block,RATNAGIRI- (M.S.) 415639, IndiaABSTRACTHigher material removal rate with better surface finish is one of the primerequirements of today’s industry. The present paper investigate the effects of cuttingparameters like spindle speed, feed and depth of cut on surface finish and material removalrate of Aluminium 7075-T6. Taguchi methodology has been applied to optimize cuttingparameters. Feed rate is the most significant factor influencing surface finish whereasmaterial removal rate is significantly affected by cutting speed. For highest MRR with bettersurface finish. Cutting speed (15.102 m/min) ,feed rate (0.3207 mm/rev.) and depth of cut(0.5 mm) are cutting parameters for higher MRR and optimum surface roughness .Keywords: Surface roughness, MRR, DOE, ANOVA, Al-7075 T6INTRODUCTIONSurface finish is the method of measuring the quality of product and is an importantparameter in machining process. It is one of the prime requirements of customers formachined parts. Productivity is also necessary to fulfill the customers demand. For thispurpose quality of product and productivity should be high. In addition to surface finishquality, the material removal rate (MRR) is also an important characteristic in turningoperation and high MRR is always desirable.Taguchi has proposed off line for quality improvement in place of an attempt toinspect quality in the product on the product line. He observed that no amount of aninspection can put quality back into the product but it merely treats a symptom. Taguchi hasINTERNATIONAL JOURNAL OF DESIGN AND MANUFACTURINGTECHNOLOGY (IJDMT)ISSN 0976 – 6995 (Print)ISSN 0976 – 7002 (Online)Volume 4, Issue 1, January- April (2013), pp. 59-67© IAEME: www.iaeme.com/ijdmt.htmlJournal Impact Factor (2013): 4.2823 (Calculated by GISI)www.jifactor.comIJDMT© I A E M E
  • 2. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME60recommended three stages to achieve the desirable product quality by design Viz. Systemdesign, Parameter design and Tolerate design system which help to identify the workinglevels of the parameter. The optimal condition is selected so that influence of noise factorscauses minimum variation to study performance. The orthogonal arrays, variance and signalto noise analysis are essential tool of parameter design.LITERATURE REVIEWJohn et al. (2001) demonstrated a systematic procedure of using Taguchi parameterdesign to optimize surface roughness performance with particular combination of cuttingparameters in end milling operation. Kopac et al. (2002) described the machining parametersinfluence and levels that provide sufficient robustness of machining process towards theachievement of the desired surface roughness for cold pre-formed steel workpieces in fineturning. Ihsan Korkut et al. (2004) carried turning tests to determine optimum machiningparameters for machining of austenitic stainless steel. Ciftci (2006) investigated themachining characteristics of austenitic stainless steel (AISI 304 and AISI 316) using coatedcarbide tools. Zhang et al. (2007) have used Taguchi method for surface finish optimizationin end milling of Aluminum blocks. G. Akhyar et al. (2008) optimized cutting parameters inturning Ti-6% Al-4% V extra low interstitial with coated and uncoated cemented carbidetools under dry cutting condition. Anirban Bhattacharya et al. (2009) estimated the effect ofcutting parameters on surface finish and power consumption during high speed machining ofAISI 1045 steel. Saeed Zare Chayoshi & Mehdi Taidari (2009) developed a surfaceroughness model in hard turning operation of AISI 4140 using CBN cutting tool.AdeelH.Suhail et al.(2010) conducted experimental study to optimize the cutting parameters usingtwo performance measures,work piece surface temperature and surface roughness.D.PhilipSelvaraj and P.Chandramohan (2010) concentrated with dry turning of AISI 304 AusteniticStainless Steel Nikolaoset al.(2010) developed a surface roughness model for turning offemoral heads from AISI 316L stainless steel.MATERIALS AND METHODSThe experimental investigation presented here was carried out on Crown lathemachine. The work piece material used for present work was AL7075-T6. The specificationused for experimentation was of Al 7075-T6. Table 1 shows Chemical composition of Al7075-T6 used for study.Table1 Chemical composition of AL7075-T6Chemical compositionLimitsWeight % Al Si Fe Cu Mn Mg Cr Zn Ti EachTotalMinimum - - 102 - 2.1 0.18 5.1 - - -Maximum Rem0.40.5 2 0.3 2.9 0.28 6.1 0.2 0.05 0.15
  • 3. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME61It was subjected to turning operation, which was carried out on Lathe machine (Crown lathemachine). As Al 7075-T6 is soft material HSS tool was selected. HSS leaves a better finishon the part and allows faster machining. HSS tool can withstand moderate temperature.Cylindrical specimen of 15mm diameter was safely turned in three jaw chuck by supportingthe free end of work. As the work piece was quite long it was needed to face and centre drillthe free end supported by the tail stock. Without such support, the force of the tool on thework piece would cause it to bend away from the tool, producing a strangely shaped result Inthis experiment,in order to investigate the surface finish of the machined workpiece andmaterial removal rate,during cutting of the AL 7075-T6,HSS tool was used.A view of thecutting zone arrangement is shown in Fig.1 The surface roughness of the finished worksurface was measured with the help of a surface roughness tester. The material,characteristicsof tool and detail of experimental design set-up are listed in Table 2 and conditions are givenin Tables 3Fig. 1 View of cutting zone (Actual arrangement and schematic arrangement)For MRR machining time for each sample has been calculated accordingly. Aftermachining, weight of each machined parts have been again measured precisely with the helpof digital balance meter.RESULTS AND DISCUSSIONTable 3 shows experimental design matrix and surface roughness value (Ra) and MRR forAl 7075-T6. S/N ratio for surface roughness is calculated using lower the bettercharacteristics and S/N ratio for MRR is calculated using larger the better characteristicshown in Table 3.The S/N ratio is calculated using equation (1) and equation (2)Machining Parameters Level 1 Level 2 Level 3Cutting speed (m/min) 9.354 15.102 23.004Depth of cut (mm) 0.1146 0.3207 0.3345Feed rate (mm/rev) 0.5 1 1.5Table 2: Machining parameters and level
  • 4. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME62ܵܰൗ ൌ െ10 log ቀଵ௡∑ ܻ݅ଶ௡௜ୀଵ ቁ -------- (1)ܵܰൗ ൌ െ10 log ቀଵ௡∑ଵ௒௜మ௡௜ୀଵ ቁ --------- (2)Table 3-Experimental design matrix and response variableExpt.TurningParameters Surface Roughness Ra S/N MRR S/NNo.Depthof cutFeedrateCuttingspeed Ra1 Ra2 Ra avg ratio Wt base ratio1 0.5 0.1146 9.354 0.67 0.64 0.655 3.67517 287.29 49.16642 0.5 0.3207 15.102 0.86 0.49 0.675 3.41392 1313.61 62.36933 0.5 0.3345 23.004 2.21 2.24 2.225 -6.9466 1996.18 66.00404 1 0.1146 15.102 0.46 0.54 0.5 6.0206 742.5 57.41395 1 0.3207 23.004 2.26 2.27 2.265 -7.1014 3011.51 69.57576 1 0.3345 9.354 0.58 0.52 0.55 -0.5877 855.5 58.64447 1.5 0.1146 23.004 1.18 0.96 1.07 5.19275 2318.14 67.30288 1.5 0.3207 9.354 2.21 1.08 1.645 -4.3233 1697.01 64.59409 1.5 0.3345 15.102 1.35 1.55 1.45 -3.2274 3208.09 70.1249Responses for Signal to Noise Ratios of Smaller is better characteristics for surfaceroughness is shown in Table 4. and Responses for Signal to Noise Ratios of larger is bettercharacteristics for MRR is shown in Table 5.Significance of machining parameters (difference between max. and min. values)indicates that feed is significantly contributing towards the machining performance asdifference gives higher values. Plot for S/N ratio shown in Figure 1 explains that there is lessvariation for change in depth of cut where as there is significant variation for change in feedrate.Significance of machining parameters (difference between max. and min. values)indicates that cutting speed is significantly contributing towards the MRR as difference giveshigher values. Plot for S/N ratio shown in Figure2 explains that there is less variation forchange in feed where as there is significant variation for change in cutting speed.
  • 5. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME63Table 4- Response Table for Signal toNoise Ratios Smaller is betterLevelDepthof cut Feed Speed1 0.0475 4.96284 -0.41192 -0.5562 -2.6703 2.069053 -0.786 -3.5872 -2.9517Delta 0.83348 8.55005 5.02079Rank 3 1 2Table 5- Response Table for Signal toNoise ratios larger is betterLevelDepthof cut Feed Speed1 59.18 57.96 57.472 61.88 65.51 63.303 67.34 64.92 67.63Delta 8.16 7.55 10.16Rank 2 3 1Table 6-Analysis of Variance for S/N ratios for surface roughnessSource DF Seq SS Adj SS Adj MS F PDepth ofcut 2 1.112 1.112 0.5559 0.03 0.975Feed 2 132.208 132.208 66.1042 3.03 0.248Speed 2 37.814 37.814 18.9071 0.87 0.536Residualerror 2 43.7 43.7 21.8502Total 8 214.8351.51.00.5420-2-40.33450.32070.114623.00415.1029.354420-2-4DepthMeanofSNratiosfeedspeedMain Effects Plot for SN ratiosData MeansSignal-to-noise: Smaller is betterFig. 2 Effect of Depth of cut, Feed rate and speed on surface finish
  • 6. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME64Taguchi method cannot judge and determine effect of individual parameters onentire process while percentage contribution of individual parameters can be well determinedusing ANOVA. MINITAB software of ANOVA module was employed to investigate effectof process parameters (Depth of cut, Feed rate and speed)Table 7-Analysis of Variance for S/N ratios for MRRSource DF Seq SS Adj SS Adj MS F PDepth ofcut 2 103.716 103.716 51.8581 828.31 0.001Feed 2 105.868 105.868 52.9338 845.49 0.001Speed 2 155.954 155.954 77.9769 1245.49 0.001Residualerror 2 0.125 0.125 0.0626Total 8 365.663Theory suggests that surface roughness is function of feed rate. In practice it is more likedirectly related to feed rate. This can be due to flattening of ridges due to side flow or toolwork relative vibrations when feed rate is lower the roughness becomes independent of feedrate.1.51.00.569666360570.33450.32070.114623.00415.1029.3546966636057DepthMeanofSNratiosFeedSpeedMain Effects Plot for SN ratiosData MeansSignal-to-noise: Larger is betterFig. 3 Effect of Depth of cut, Feed rate and speed on MRR
  • 7. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME65Table 6 shows Analysis of variance for S/N ratio. F value (3.03) for S/N ratioparameter indicates that feed rate is significantly contributing towards machiningperformance. F value (0.03) for S/N ratio of parameter indicates that depth of cut iscontributing less towards surface finish. It can be observed rough surface from surface texturefor the specimen No.5 (cutting speed 23.004 m/min; depth of cut 1 mm; feed 0.3207mm/rev.) and smooth surface for the specimen No.4 (cutting speed 15.102 m/min; depth ofcut 1 mm; feed 0. 1146 mm/rev.) .Fig. 5 Specimen with higher MRR and optimum surface roughnessTable 7 shows analysis of variance S/N ratio for MRR. F value (1245.49) for S/Nratio parameter indicates that cutting speed is significantly contributing towards MRR. Fvalue (828.31) for S/N ratio of parameter indicates that depth of cut is contributing lesstowards MRR. It was observed that maximum MRR is obtained at the cutting speed(15.102m/min), feed rate (0.3345mm/rev) and depth of cut (1.5mm)Fig 4 Graph of surface roughness and MRR vs Expt. No.010002000300040001 2 3 4 5 6 7 8 9MRRMRRPower (MRR)00.511.522.51 2 3 4 5 6 7 8 9S/F ROUGHNESSS/F ROUGHNESSPower (S/FROUGHNESS)Power (S/FROUGHNESS)
  • 8. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME66The power regression type is used to calculate the trend of each graph. Byobserving the graph of surface roughness vs experiment found that the optimum values ofsurface roughness lies in between 0.6 to 1.2 µm and for MRR lies in between 1000 to 2000mm3/min.CONCLUSIONFollowing are the conclusions drawn based on the test conducted on Al 7075-T6alloy during Turning operation with HSS1. From response Table for Signal to Noise ratios for surface roughness, based on theranking it can be concluded that Feed has a greater influence on the Surface Roughnessfollowed by Speed. Depth of Cut had least influence on Surface Roughness.2. From response Table for Signal to Noise ratios for MRR, based on the ranking it can beconcluded that cutting speed has a greater influence on the Surface MRR followed byfeed rate. Depth of Cut had least influence on MRR3. Cutting speed (15.102 m/min), feed rate (0.3207 mm/rev.) and depth of cut (0.5 mm) arecutting parameters for higher MRR with better surface finish.ACKNOWLEDGEMENTQuality control department of Adler Mediequip PVT.LTD, Ratnagiri are gratefullyacknowledged.REFERENCES1 Anirban Bhattacharya, Santanu Das, P. Majumdar (2009), AjayBatish, Estimating theeffect of cutting parameters on surface finish and power consumption during high speedmachining of AISI 1045 steel during Taguchi design and ANOVA, Prod. Eng. Res.Devel, vol 3, pp 31-402 Adeel H. Suhail, N. Ismail, S.V.Wong and N. A. Abdul Jalil (2010), Optimisation ofcutting parameters based on surface roughness and assistance of workpiece surfacetemperature in turning process, American journal of engineering and applied sciences,vol.3 (1), pp 102-1083 Ciftci I. (2006), Machining of austenitic stainless steels using cvd multilayer coatedcementet carbide tools, Tribogy, Internation, vol 39 (6), pp 565-5694 D. Philip Selvaraj and P. Chandermohan (2010), Optimisation of surface roughnes AISI304 Austenti stainless steel in dry operation using Taguchi method, Journal ofengineering science and technology, vol 5, pp 293-3015 G. Akhyar, C. H. Cheharon and J. A. Ghani (2008), Application of Taguchi method in theoptimization of turning parameters for surface roughness, International Journal of ScienceEngineering And Technology, vol. 1 (3), pp 1379-1385J.6 Kopac J. ,M. Bahor and M. Sokovic (2002), Optical machining parameters for achievingthe desired surface roughness in fine turning of cold preformed steel workpiecs, MachineTool Manufacturing, vol 42, pp 707-7167 Korkut I., Kasap M. Ciftci I. and Seker U (2004), Determination of optimum cuttingparameters during machining of AISI 304, Austenitic stainless steel, Materials andDesign, vol. 25(4), pp 303-305
  • 9. International Journal of Design and Manufacturing Technology (IJDMT), ISSN 0976 –6995(Print), ISSN 0976 – 7002(Online) Volume 4, Issue 1, January- April (2013), © IAEME678 Gulhane U. D. A. B. Dixit, P. V. Bane and G. S. Salvi, “Optimization of processparameters for 316L stainless steel by using Taguchi method and ANOVA”, InternationalJournal of Mechanical Engineering and Technology (IJMET), Volume 3, Issue 2,PP. 67-72, ISSN Print : 0976 - 6340, ISSN Online: 0976 – 6359.9 M. Kaladhar, K. V. Subbaiah, Ch. Srinivasa Rao and K. Narayana Rao (2011),Application of Taguchi approach and utility concept in solving the multi-objectiveproblem when turning AISI 202 Austenitic stainless, Journal of Engineering Science AndTechnology, vol 4 (1), pp 55-6110 Nikolaos I, Galanis and Dimitrios E. Manolakos (2010), Surface roughness prediction inturning of femoral head, Intenational journal of advmanufacturing technology, doi1007/s00170-010-2616-411 T.G.Ansalam Raj and V.N. Narayanan Namboothiri (2010), An improved geneticalgorithm for the prediction of surface finish in dry turning of SS 420 materials,Manufacturing Technology Today vol.47, p 313-32412 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), PP.233-23913 Gulhane U. D., Mishra S. B. and Mishra P. K., “Enhancement of surface roughness of316 L Stainless Steel and Ti-6Al-4V using Low Plasticity Burnishing: DOE Approach”International Journal of Mechanical Engineering and Technology (IJMET), Volume 3,Issue 1, pp. 150-160, ISSN Print : 0976 - 6340, ISSN Online: 0976 – 6359.14 P.B.Wagh, R.R.Deshmukh And S.D.Deshmukh, “Process Parameters Optimization forSurface Roughness in EDM for AISI D2 Steel by Response Surface Methodology”,International Journal of Mechanical Engineering & Technology (IJMET), Volume 4,Issue 1, 2013, pp. 203 - 208, ISSN Print : 0976 - 6340, ISSN Online: 0976 – 6359.14 U.D.Gulhane, M.P.Bhagwat, M.S.Chavan, S.A.Dhatkar and S.U.Mayekar,“Investigating the Effect of Machining Parameters on Surface Roughness of 6061Aluminium Alloy in End Milling”, International Journal of Mechanical Engineering &Technology (IJMET), Volume 4, Issue 2, 2013, pp. 134 - 140, ISSN Print : 0976 - 6340,ISSN Online: 0976 – 6359.

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