Investigating Effect of Machining Parameters of CNC ... - IRD India

Investigating Effect of Machining Parameters of CNCMilling on Surface Finish by Taguchi MethodAmit Joshi & Pradeep KothiyalMechanical Engg. Deptt., G.B.Pant Engineering College, Pauri Garhwal, IndiaE-mail : amitjoshi_81@yahoo.co.in & pradeep201183@gmail.comAbstract - CNC End milling is a unique adaption of theconventional milling process which uses an end mill toolfor the machining process. CNC Vertical End MillingMachining is a widely accepted material removal processused to manufacture components with complicated shapesand profiles. During the End milling process, the material isremoved by the end mill cutter. The effects of variousparameters of end milling process like spindle speed, depthof cut, feed rate have been investigated to reveal theirImpact on surface finish using Taguchi Methodology.Experimental plan is performed by a Standard OrthogonalArray. The results of analysis of variance (ANOVA) indicatethat the feed Rate is most influencing factor for modelingsurface finish. The graph of S-N Ratio indicates the optimalsetting of the machining parameter which gives theoptimum value of surface finish. The optimal set of processparameters has also been predicted to maximize thesurface finish.Among several CNC industrial machiningprocesses, milling is a fundamental machiningoperation. End milling is the most common metalremoval operation encountered. It is widely used in avariety of manufacturing in industries. The quality ofthe surface plays a very important role in theperformance of milling as a good-quality milledsurface significantly improves fatigue strength,corrosion resistance, or creep life. The surfacegenerated during milling is affected by different factorssuch as vibration, spindle run–out, temperature, toolgeometry, feed, cross-feed, tool path and otherparameters.Keywords— surface finish, orthogonal array, taguchimethod, ANOVA, SN ratio.I. INTRODUCTIONIn present time the technology of CNC verticalmilling machine has been improved significantly tomeet the advance requirements in variousmanufacturing fields, especially in the precision metalcutting industry. This experiment gives the effect ofdifferent machining parameters (spindle speed, feed,and depth of cut) on material removal rate in endmilling. The demand for high quality and fullyautomated production focus attention on the surfacecondition of the product, surface finish of the machinedsurface is most important due to its effect on productappearance, function, and reliability. For these reasonsit is important to maintain consistent tolerances andsurface finish.During finish milling, the depth of cut is small.Technological parameter range plays a very importantrole on surface roughness. In end milling, use of highcutting speed, low feed rate and low depth of cut arerecommended to obtained better surface finish for thespecific test range in a specified material. Materialremoval rate (MRR) is an important control factor ofmachining operation and the control of machining rateISSN : 2319 – 3182, Volume-1, Issue-2, 201260

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME)is also critical for production planners. MRR is ameasurement of productivity & it can be expressed byanalytical derivation as the product of the width of cut,the feed velocity of milling cutter and depth of cut.Cutting feed is the most dominated factor for surfacefinish. The most important interactions, that effectsurface roughness of machined surfaces, are betweenthe cutting feed and depth of cut, and between cuttingfeed and spindle speed. Surface Roughness is affectednegatively if the applied force is increased. Surfaceroughness at the same feed rate becomes higher when asmall nose radius is used. Effort to increaseproductivity and MRR was maximized by optimalselection of feed rate, geometric boundary conditions].With the more precise demands of modernengineering products, the control of surface texturetogether with dimensional accuracy has become moreimportant. This experimental investigation outlines theTaguchi optimization methodology, which is applied tooptimize MRR in end milling operation. Theexperiment is conducted on aluminium cast heattreatablealloy the processing of the job is done byHigh Speed Steel (HSS) end-mill tool under finishingconditions. The machining parameters evaluated arespindle speed, feed rate and depth of cut. Theexperiments are conducted by using Taguchi L 9orthogonal array as suggested by Taguchi. Signal-to-Noise (S/N) ratio and Analysis of vVariance(ANOVA) is employed to analyse the effect of millingparameters on material removal rate.II. EXPERIMENT AND DATA COLLECTIONExperiments are designed with the help of usingtaguchi L 9 orthogonal array. The software used forDOE (Design of experiment) is Minitab15. Experimentis divided into three main phases. These three phasesare the planning phase, the conducting phase, theanalysis phase.A) Planning phase2.1 input parameters & there levels for End MillingTable 1Control factorSpindle speedFeed RateDepth of cutParameters of the settingSymbolFactor AFactor BFactor CTable 2Controlfactors/levelsSelected input ParameterLevel1 Level2 Level3 UnitsFactor A 800 1000 1200 rpmFactor B 60 80 100 mm/minFactor C 0.2 0.3 0.4 mm2.2 Design of experiments (DOE)For selected input parameters experiments aredesigned using Taguchi L 9 orthogonal standard array.For this purpose software Minitab 15 is usedExperimentno1(A)Spindle speed(RPM)TABLE-3 (DOE)2(B)Feed Rate(mm/min)3(C)Depth of cut(mm)1 800 60 0.22 800 80 0.33 800 100 0.44 1000 60 0.35 1000 80 0.46 1000 100 0.47 1200 60 0.48 1200 80 0.29 1200 100 0.3The set of 9 experiments are shown in table-3.2.3 Workpiece materialThe material used for the experiment is (100 x 34x 20 mm) 5 blocks of aluminium cast heat-treatablealloy. Whose compositions are as follows.COMPOSITION(% By Weight)Yield Strength (MPa) 110Tensile Strength(MPa) 221Ductility (%EL in 50 mm) 8.5Cu 4.5%Si 1.1%Rest AlApplications Flywheel and rear axlehousing, Bus and aircraftwheels, Crankcases61ISSN : 2319 – 3182, Volume-1, Issue-2, 2012

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME)B. Conducting phase2.4 ExperimentationAfter DOE, 9 experiments are carried out in CNCvertical End milling. After each experiment SURFACEROUGHNESS is calculated. A quality characteristicfor SURFACE ROUGHNESS is smaller is the better.The signal-to-noise ratios of each experimental run arecalculated based on the following equation, which arelisted in corresponding tables with the data.The equation is:-The specification of this instrument are given inbelow.Specification of surftest SJ201P:DetectorDetection methodMeasurement rangedifferential inductancemethod350µm(-200µm to +150µm)13780µin (-7880µin to+5900µin)Where SNi is the signal to noise ratio of ith term,n = number of measurements in a trial/row, in thiscase, n=3 and Yi is the i th measured value in a run/row.Instruments used for measuring surface roughnessOne of the measurable output characteristics issurface Roughness. Instrument used in this paper formeasurement of surface Roughness is MitutoyoSurftest SJ-201P. The surftest SJ-201P (mitutoyo) is ashop–floor type surface-roughness measuringinstrument, which traces the surface various machineparts and calculates the surface roughness based onroughness standards, and displays the results. Theworkpiece is attached to the detector unit of the SJ-201P will trace the minute irregularities of theworkpiece surface. The vertical stylus displacementduring the trace is processed and digitally displayed onthe liquid crystal display of the SJ-201P.Stylus materialTip radiusMeasuring forceRadius of skidcurvatureDiamond5µm (2900µin), 2µm (8µin)0.75mN (measuring forcetype)4mN (0.4gf), 0.75mN(0.75gf)(0.75mN measuring forcetype)40mm (1.57 in)Drive unitMeasurement range and resolutionFig 1 Mitutoyo Surftest SJ-201P InstrumentDetector drive rangeTraversing speedmeasurementReturnDetector retractionfunctionBottom configuration21 mm (.82 in)0.25mm/s, 0.5mm/s(.01in/s, .02in/s)0.8mm/s (.03in/s)Stylus upV-way62ISSN : 2319 – 3182, Volume-1, Issue-2, 2012

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME)Measurement rangeResolutionAutoo.o1 µm to .04 µm dependingon the measurementrange350 µm (14000 µin) 0.4 µm (12.8 µin )100 µm (4000 µin) 0.1 µm (3.2 µin)50 µm (2000 µin) 0.05 µm (1.6 µin)10 µm (400 µin) 0.01 µm (.4 µin)Table for response (surface roughness) and S-Nratio is shown in table-4FeedrateDepth ofcutResidualerror2 0.2231 0.2231 0.1115 7.02 0.1252 29.0653 29.0653 14.5327 915.28 0.0012 0.0318 0.0318 0.0159total 8 29.7445From above table it can be seen that all the threefactors are significantly affect the response. Feed ratehas highest effect on the response spindle speed hassecond highest effect on response and depth of cut hasleast effect on response.(A)Spindlespeed(rpm)TABLE-4(B) (C)Feed rate Depth(mm/min) of cut(mm)ResponseSurfaceroughness(µm)S-NRatio(dbi)1 800 60 0.2 1.37000 -2.735032 800 80 0.3 1.80000 -5.105813 800 100 0.4 2.10000 -6.444654 1000 60 0.3 0.77000 2.268235 1000 80 0.4 0.79000 2.045606 1000 100 0.2 0.89000 1.010747 1200 60 0.4 0.35000 9.109208 1200 80 0.2 0.52000 5.675659 1200 100 0.3 3.09000 -2.32437C) Analysing Phase2.5 Analysis of variance (ANOVA)The output characteristic, surface finish isanalysed by software Minitab 15 and ANOVA isformed, which shows the percentage contribution ofeach influencing factor on surface roughness. This alsosignifies that which factor is more predominant in CNCEND MILLING. Main effect plots for means and Maineffect plots for SN ratios are plotted by help ofsoftware Minitab 15.TABLE 5 Analysis of variance for meanssource DF Seq SS Adj SS Adj MS F PSpindlespeed2 0.4243 0.4243 0.2121 13.36 0.070The figure 2 contains the graph between SN ratiodata and input parameter. The figure 3 contain thegraph between mean and control factors. The objectiveof using the SN ratio as a performance measurement isto develop product and processes in sensitive to noise63ISSN : 2319 – 3182, Volume-1, Issue-2, 2012

International Journal on Theoretical and Applied Research in Mechanical Engineering (IJTARME)factor. The optimal combination of input machiningparameter can be observed from fig 2 i.e. graphbetween S-N ratio and input parameter, The optimalsetting of input parameter is(A 1 B 3 C 2 ). The processparameter setting with the highest SN ratio alwaysyields the optimum quality with minimum variation(Antony & Kaye, 1999).2.6 COMPUTATION OF AVERAGEPERFORMANCEAverage performance of each factor on surfaceroughness is calculated byFollowing expressionTABLE 7 Response table for MeanLEVEL SPINDLESPEEDFEEDRATEDEPTHOF CUT1 1.7567 0.8300 0.92672 0.8167 1.0367 1.29223 0.7256 1.4322 1.0800DELTA 1.0311 0.6022 0.3656RANK 1 2 32.7 CALCULATION OF OPTIMUM SURFACEFINISH.Let T ’ = average result for 9 runs of SURFACEFINISH=1.2977Similar expression can be written for calculating theaverage performance of factor B & C.Notations:-A’ 1 , A’ 2 , A’ 3 = average performance of factor A at level1, 2, 3 respectivelyB’ 1 , B’ 2 , B’ 3 = average performance of factor B at level1, 2, 3 respectivelyC’ 1 , C’ 2 , C’ 3 = average performance of factor C at level1, 2, 3 respectivelyTable for factors A, B, C & their levels 1, 2, 3 withSurface Roughness is shown below.A’ 1 = 1.75A’ 2 = 0.81A’ 3 = 1.32B’ 1 = 0.83B’ 2 = 1.04B’ 3 = 2.03C’ 1 = 0.93C’ 2 = 1.88C’ 3 = 1.08TABLE 6 Response table for Signal to Noise RatioLEVEL SPINDLESPEEDFEEDRATEDEPTHOF CUT1 -4.7618 2.8808 1.31712 1.7749 0.8718 -1.72063 4.1535 -2.5861 1.5700DELTA 8.9153 5.4669 3.2907RANK 1 2 3SF OPT = T’+ (A 1 – T’) + (B 3 -T’) + (C 2 -T’)=3.0723 µm3. RESULT AND DISCUSSION1) From the graph of S-N ratio it can be observed thatoptimal value of surface finish is obtained at firstlevel of factor A, third level of factor B and secondlevel of factor C.2) Optimal value of surface finish is 3.0723 µm.3) From the ANOVA it can be seen that percentagecontribution of feed rate is maximum and it meansFeed rate is the most dominating factor formodelling surface finish.4) Taguchi robust design is suitable for modellingsurface finish in CNC milling.REFERENCES1] Dalgobind Mahto And Anjani Kumar,” Optimization ofProcess Parameters in Vertical CNC Mill MachinesUsing Taguchi’s Design of Experiments”, Ariser Vol. 4No. 2 (61-75,20082] Tao Ye, Cai-Hua Xiong,” Geometric ParameterOptimization In Multi-Axis Machining”, Computer-Aided Design 40 (2008) 879–890,20083] S. Doruk Merdol, Yusuf Alt,” Virtual Cutting AndOptimization Of Three-Axis Milling Processes”,International Journal Of Machine Tools & Manufacture48 1063– 1071,20084] H.-S. Lu, J.-Y. Chen, Ch.-T. Chung,” The OptimalCutting Parameter Design Of Rough Cutting Process InSide Milling”, Volume 29 Issue 2 , 20085] M.A. Lajis, A.N. Mustafizul Karim, A.K.M. NurulAmin, A.M.K. Hafiz, L.G. Turnad,” Prediction Of Tool64ISSN : 2319 – 3182, Volume-1, Issue-2, 2012

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