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Investigating the effect of machining parameters on surface roughness

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Investigating the effect of machining parameters on surface roughness

  1. 1. INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 – International Journal of JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 4, Issue 2, March - April (2013), pp. 134-140© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2013): 5.7731 (Calculated by GISI) ©IAEMEwww.jifactor.com INVESTIGATING THE EFFECT OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF 6061 ALUMINIUM ALLOY IN END MILLING U. D. Gulhane*, M.P.Bhagwat, M.S.Chavan ,S.A.Dhatkar, S.U.Mayekar Department 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, India ABSTRACT Design of experiments is performed to analyse the effect of spindle speed, feed rate and depth of cut on the surface roughness of 6061 Aluminium alloy. The results of the machining experiments were used to characterise the main factors affecting surface roughness by the Analysis of Variance (ANOVA) method. The feed rate was found to be the most significant parameter influencing the surface roughness in the end milling process. Keywords: Surface roughness, DOE, ANOVA, 6061 Aluminium alloy. INTRODUCTION Milling process is one of the common metal cutting operations used for machining parts in manufacturing industry. It is usually performed at the final stage in manufacturing a product. The demand for high quality and fully automated production focuses attention on the surface condition of the product, especially the roughness of the machined surface, because of its effect on product appearance, function, and reliability. In the present work an experimental investigation of milling on aluminium 6061 with HSS tool is carried out and the effect of different cutting parameters on the surface roughness is studied. 134
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME The material used for the analysis is 6061 Aluminium alloy which is widely used inwings of the aeroplane, wheels of the automobiles. In this paper, L9 orthogonal array is employed to analyze experimental results ofmachining obtained from 9 experiments by varying three process parameters viz. cuttingspeed (A), depth of cut (B) and feed rate(C). ANOVA has been employed and compared withTaguchi method.METHODOLOGY DOE techniques enable designers to determine simultaneously the individuals andinteractive effects of many factors that could affect the output results in any design. There arethree input parameters and three level. Full factorial experimental design will give rise tototal 33=27 experiments which is time consuming and lengthy procedure. Fig 1: End- milling operation Taguchi found out new method of conducting the design of experiments whichare based on well defined guidelines. This method uses a special set of arrays calledorthogonal array. This standard array gives a way of conducting the minimum number ofexperiments which could give the full information of all the factors that affect the responseparameter instead of doing all experiments. ANOVA was developed by Sir Ronald Fisher in 1930 and can be useful fordetermining influence of any given input parameter for a series of experimental results bydesign of experiments for machining process and it can be used to interpret experimentaldata. ANOVA is statistical based objective decision making tool for detecting any differencesin average performance of groups of items tested. While performing ANOVA degrees offreedom should also be considered together with each sum of squares. In ANOVA studies acertain test error, error variance determination is very important. Obtained data are used toestimate F value of Fisher Test (F-test). Variation observed (total) in an experimentalattributed to each significant factor or interaction is reflected in percent contribution (P),which shows relative power of factor or interaction to reduce variation. 135
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMEMATERIALS AND METHOD The Rectangular 25 X 25 X 100 mm 6061 Aluminium Alloy specimens were used forexperimentation. Table 1 and 2 shows properties and composition of 6061 Aluminium Alloyused for the study. Milling operation was carried out on SINGER UNIVERSAL MILLINGMACHINE by using HSS tool. Table 1 Properties of 6061 Al TENSILE TEST BHN Ultimate Yield Modulus Of For 500 Stress Stress Elasticity Kg (N/mm2) (N/mm2) (GPa) 251.66 202.92 56.1 79.57 Table 2 Composition of 6061 Al Elements Al Si Fe Mg Ti Ca Cd B P Na Mn % 98.81 0.475 0.178 0.49 0.0135 0.0027 0.001 0.0015 0.0014 0.0043 0.0005 Work piece was inserted in the jaw on the work bed and was tightened in the jawsuntil they fixed the work piece such that top surface of the work piece will be perfectlyperpendicular to the tool axis. The milling was carried out for 9 different work pieces. Foreach workpiece, all the three parameters, viz. cutting speed, depth of cut and feed rate, werevaried as shown in Table 3. Table 3: Machining parameters and levels: Machining Level 1 Level 2 Level 3 Parameters Cutting speed 58 220 500 (Rev/min) Depth of cut 0.4 0.8 1.2 (mm) Feed rate 15.31 41.84 104.56 (mm/min) The surface roughness of each specimen was tested on the surface roughness tester(Mitutoyo Roughness tester SJ-400) for cut off value of 4.0 mm distance. The Ra value wasgenerated by the tester for each work piece. 136
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMERESULTS AND DISCUSSION Table 4 shows experimental design matrix and surface roughness value (Ra) for 6061Al, S/N ratio is calculated using Lower the better characteristics.where, n = No of measurements in a trial/row Yi = ith measured value in a run/row Table 4 Experimental Design Matrix and Results EXPT NO. MILLING PARAMETERS DEPTH FEED S/N SPEED OF CUT RATE SURFACE RATIO MEAN (RPM) (mm) (mm/min) ROUGHNESS (µm) 1 58 0.4 15.32 2.54 -8.0967 2.54 2 58 0.8 41.84 2.78 -8.8809 2.78 3 58 1.2 104.56 2.97 -9.4551 2.97 4 220 0.8 15.32 2.06 -6.2773 2.06 5 220 1.2 41.84 2.86 -9.1273 2.86 6 220 0.4 104.56 3.4 -10.6296 3.4 7 500 1.2 15.32 1.58 -3.9731 1.58 8 500 0.4 41.84 1.81 -5.1536 1.81 9 500 0.8 104.56 2.54 -8.0967 2.54 Responses for Signal to Noise Ratios of Smaller is better characteristics is shown inTable 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. Table 5-Response Table for a) Signal to Noise Ratios and (b) means (a) (b) Level A B C Level A B C 1 -8.811 -7.96 -6.116 1 2.763 2.583 2.06 2 -8.678 -7.752 -7.721 2 2.773 2.46 2.483 3 -5.741 -7.519 -9.394 3 1.977 2.47 2.97 Delta 3.07 0.441 3.278 Delta 0.797 0.123 0.91 Rank 2 3 1 Rank 2 3 1 137
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME M a in E ff e c ts P lo t f o r S N r a tio s D a ta M e a n s Speed Fe e d -6 -7 -8 Mean of SN ratios -9 58 220 500 1 5 .3 3 4 1 .9 2 1 0 4 .9 0 Depth -6 -7 -8 -9 0 .4 0 .8 1 .2 S ig n a l - to - n o is e : S m a l le r is b e tte r M a in E f f e c t s P l o t f o r M e a n s Da ta M e a n s Speed Fe e d 3 .0 0 2 .7 5 2 .5 0 2 .2 5 Mean of Means 2 .0 0 58 220 500 1 5 .3 3 4 1 .9 2 1 0 4 .9 0 De pth 3 .0 0 2 .7 5 2 .5 0 2 .2 5 2 .0 0 0 .4 0 .8 1 .2 Fig. 2 Effect of cutting speed, Depth of cut and Feed rate on surface finish Taguchi 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 cutting speed, Depth of Cut and Feed rate. Table 6-Analysis of Variance for S/N ratios Adj Source DF Seq SS Adj SS MS F P A 2 18.0668 18.0668 9.0334 5.46 0.155 B 2 0.2926 0.2926 0.1463 0.09 0.919 C 2 16.121 16.121 8.0605 4.87 0.17 Residual error 2 3.3098 3.3098 1.6549 Total 8 37.7902 138
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table 7-Analysis of Variance for Means Source DF Seq SS Adj SS Adj MS F P Speed 2 1.25362 1.25362 0.62681 5.4 0.156 Feed 2 1.24416 1.24416 0.62208 5.36 0.157 Depth 2 0.02816 0.02816 0.01408 0.12 0.892 Residual Error 2 0.23209 0.23209 0.11604 Total 8 2.75802Table 6 and 7 shows Analysis of variance for S/N ratio and mean. F value (5.46) of parameterindicates that feed rate is significantly contributing towards machining performance. F value(0.09) of parameter indicates that depth of cut is contributing less towards surface finish. Itcan be observed rough surface for the specimen No. 6 (cutting speed, 220 rev/min; depth ofcut, 0.4 mm; feed, 104.90 mm/min.) and smooth surface for the specimen No. 7 (cuttingspeed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/min.) Fig3: Surface texture for the test (cutting Fig 4: Surface texture for the test (cutting speed 500 rev/min, depth of cut 1.2 mm, speed 220 rev/min, depth of cut 0.4 mm, feed 15.33 mm/min) feed 104.90 mm/min) Fig5: Surface Roughness Profile For specimen 1 Cut off length = 4.0 mm, Ra= 2.54 µm The pattern of impressions left by tool after the machining of workpiece is called as a‘lay pattern’ and it is circular in end milling process. When the feed is high the pattern ismore prominent as the time available for traversing is less. When the feed is low the laypattern is not much emphasized as more time available for traversing. Hence we observed inour experimentation that the contribution of feed is dominant amongst all three parameters insurface roughness. 139
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMECONCLUSION Taguchi method of experimental design has been applied for investigating the effectof machining parameters on surface roughness. Results obtained from Taguchi methodclosely matches with ANOVA. Best parameters found for finish machining are: cuttingspeed, 500 rev/min; depth of cut, 1.2 mm; feed, 15.33 mm/rev. The parameters found forrough machining are cutting speed, 220 rev/min; depth of cut, 0.4 mm; feed, 104.90 mm/min.Feed is most influencing parameters corresponding to the quality characteristics of surfaceroughness.ACKNOWLEDGEMENT Quality control department of Adler Mediequit PVT.LTD, Ratnagiri are gratefullyacknowledged.REFERENCES1. Gulhane U. D., et. al.(2012),” Improvement in surface roughness of 316 L Stainless Steel andTi-6Al-4V: DOE Appproach” International Journal of Mechanical Engineering and Technology,2012, Volume 3, Issue 1, pp. 150 - 160, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.2. Patel K.P. (2012) “Experimental analysis on surface roughness of CNC end milling processusing taguchi design method” International Journal Of Engineering Science And Technology(IJEST) Vol-4 No.02 ISSN:0975-5462.3. Mohammed T., et. al. (2007) “A Study of the effects of machining parameters on the surfaceroughness in the end milling process.”, Jordan Journal Of Mechanical And IndustrialEngineering.(JJMIE) Vol-1No-1. ISSN-1995-665.4. Kuttolamadom M. A., et. al (2010), “ Effect Of machining feed on surface roughness incutting 6061 aluminum” 2010-01-0218.5. Kakati A., et. al. (2011), “ Prediction of optimum cutting parameters to obtain desired surfacein finish pass end milling of aluminium alloy with carbide tool using ANN” World Academy OfScience And Engineering and Technology 81.6. Gopalsamy B. M., et. al. (2009), ”taguchi method and Anova : An Approach for processparameters optimisation of hard machining while machining hardened steel” Journal of Scientificand Industrial research, vol.68, pp.686-695.7. Julian J. Faraway, Practical Regression and ANOVA using R, July 2002, pp 168-200.8. Phillip J. Ross “ Taguchi Techniques for Quality Engineering” Printed and bounded by R.R.Donnelley and son’s company 2nd edition.9. Gulhane U. D. , et. al. (2012), “Optimization of process parameters for 316L stainless steelby using Taguchi method and ANOVA” International Journal of Mechanical Engineering andTechnology(IJMET). Volume 3, Issue 2, pp. 67-72, ISSN Print: 0976 – 6340, ISSN Online:0976 – 6359.10. P.C. Sharma “A text book of production engineering” ISBN: 81-219-0111, Code:10A 038A.11. N.B.Doddapattar and N Lakshmana swamy, “An Optimization of Machinability ofAluminium Alloy 7075 and Cutting Tool Parameters by using Taguchi Technique” InternationalJournal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012,pp. 480 - 493, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359 140

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