Optimization of machining parameters on milling of GFRP composites by GREY Method

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Optimization of machining parameters on milling of GFRP composites by GREY Method

Arpit

1 , Purushottam sahu2

1BM College of technology Indore, RGPV, Bhopal

2Example: Professor, Dept. of Mechanical Engineering, BMCollegeoftechnologyIndore ***

Abstract

Glass fiber reinforced polymer (GFRP) composite gear is used in a variety of applications that require precise motion transferandquietrotation.Tobroadenitsapplication,thegear'squalitymustbeimproved. Theanalysiswascarriedout to assess the influences of the machining factors on the responses in order to determine the significant effect of the parameters on the quality characteristics of the test. This analysis was carried out with a level of significance of 5%, implying a level of confidence of 95%. Surface roughness ANOVA result It is discovered that the Cutting Tool (P=0.003) (47.73 percent) has the greatest influence on the machining force. Spindle speed (P= 0.048) (18.30 percent), Feed rate (0.098) (13.30 percent), and Depth of cut (0.136) (11.27 percent) were the next most significant. Surface roughness is a significantfactorintheparametersofthecurrentstudy.

1.1 INTRODUCTION:

TheGRA basedTaguchioptimization technique with experimental design aidsin thecontrol/reductionofvarious errors duringthefabricationofGFRPcompositegear.(3)Theresponsetablewasused toselectvariousparametersfor ranking. Thep valueislessthan0.02andthusallprocessparametershaveasignificanteffectontheperformancecharacteristicsof GFRP. Rotary feed, cutting speed, cutting fluid ratio, and cutting fluid flow rate have been identified as key significant parametersofthegearshapermachinethatcontrolperformancecharacteristics.Theoptimummachiningparametersare 0.15mm/strokerotaryfeed,240strokes/mincuttingspeed,12%cuttingfluidratio,and30ml/mincuttingfluidflowrate. Thesignificantparametersthatinfluenceperformancecharacteristicshavea96%confidencelevel. Accordingtoareview of the existing literature, many studies have been conducted on polymer composite gears performance based on milling machine by varying reinforcement, process routes, and gear pair combinations of different materials, but no study is available on single optimization of milling machine cutting parameters for minimum variation/deviation of surface roughness of tooth, which affects noise, vibration, and load carrying capacity. The variation in root diameter affects the root fillet radius, which is responsible for tooth beam strength; the variation in tooth thickness controls the proper meshingofteeth, whichis responsible for noiseand vibration; and the variationin surfaceroughnessaffectsthefriction andlifeofteethinwear.

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2. METHODOLOGY

2.1MaterialsandMethodology

A total of 25 tests were scheduled. The L25 orthogonal array architecture developed by Taguchi was used. Four critical parameterswereconsideredwhenorganizingthetests:spindlespeed,feedrate,depthofcut,andtypeofmillingtoolused in the milling process; each parameter was adjusted at five levels. During the Taguchi study, the average value of experimental response and its related signal to noise (S/N) ratio for each run were obtained to investigate the effects of variousmachiningprocessTheS/NratiowaschosenfortheTaguchianalysisbecauseitaidsindescribingthetrialdata's average(mean)andvariables(standarddeviation).Thegoalofthisstudywastoachievethelowestmachining forceand surfaceroughnesspossiblebyoptimizingtheinputprocessparameters.Asaresult,theS/Nratiowillfallintothecategory of "lower is better." Experiments were carried out on a universal milling machine with a spindle power of 10 kW and a maximumspeedof3000rpm.Tokeeptheworkpiececentralandavoidvibrations,aspeciallydesignedfixturewasused. [10]

2.2 Properties of GFRP Engineering polymer of DuPont make Zytel 70G33LN010 with 33% glass fibre reinforced polyamide66resinhasbeenchosenformakinggearblanks,anddetailsofitspropertiesaregiveninTable1.

TheTaguchimethodisatechniquefordesigningqualitysystemsbasedonorthogonalarrays,whichprovideslessvariance forexperimentswithoptimalparametersettings.

5. Optimization methodology using grey relational analysis

Step 1

Thedataisfirsttobenormalizedbecauseofavoidingdifferentunitsandtoreducethevariability.Itisessentiallyrequired sincethevariationofonedatadiffersfromotherdata.Asuitablevalueisderivedfromtheoriginalvaluetomakethearray between0to1(NoorulHaqetal.,2008).Ingeneral,itisamethodofconvertingtheoriginaldatatoacomparabledata.If the response is to be minimized, then smaller the better characteristics is intended for normalization to scale it into an acceptablerangebythefollowingformula.

Equationisselectedanditcanbeexpressedas

(1) Thefirststandardizedformulaissuitableforthebenefit typefactor.

(2) Thesecondstandardized formulaissuitablefordefect typefactor.

Thethirdstandardizedformulaissuitableforthemedium typefactor. Thegreyrelationdegreecanbecalculatedbystepsasfollows: a) Theabsolutedifferenceofthecomparedseriesandthereferentialseriesshouldbeobtainedbyusingthe followingformula[15]:

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

Andthemaximumandtheminimumdifferenceshouldbefound.

b) Thedistinguishingcoefficient p isbetween0and1.Generally,thedistinguishingcoefficient p issetto0.5.

c) Calculationoftherelationalcoefficientandrelationaldegreeby(12)asfollows. InGreyrelationalanalysis,Greyrelationalcoefficient canbeexpressedasfollows[15]: ()max ()minmax 

Andthentherelationaldegreefollowsas:

In equation (13), is the Grey relational coefficient, w (k) is the proportion of the number k influence factor to the total influenceindicators.Thesumof w (k) is100%.Theresultobtainedwhenusing(12)canbeappliedtomeasurethequality ofthelistedsoftwareprojects.

Step 6

After optimal combinationof processparametersarefoundout, the nextstepisverytheimprovementof grey relational gradethroughconductingconfirmatoryexperiment.Thepredictedvalueofgreyrelational gradeforoptimal level canbe obtainedasfollows, 0 1 im i m 

Where m isthetotalmeangreyrelationalgrade, i isthemeangreyrelationalgradeattheoptimallevelofeach parameter,andthenumberofthesignificantprocessparameters(Sahoo&Sahoo,2013).

The predicted optimumvaluesarelistedinTable 5.9. Thevalue of  iscalculated0.4413fromabove equation.Tocheck thereliabilityofpredictedGRG,ConfidenceInterval(CI)isalsodeterminedusingEquation(7)(Çaydaş&Hasçalık,2008; Ju Long,1982).

5.1. Implementation of methodology to

Step 1

find

multi response parametric optimization

The experimental data have been normalized for Material removal rate (Gm. /Min), Total metal removed presented in Table 5.1 called grey relationalgenerations.Pre processing is important in GRA because the data sequences that will be compared have different ranges and units. Therefore, all data sequences must be standardized between the same lower andupperlimits.Theyaredefinedhereas 0and1.ForLargertheBetterCharacteristics(e.g.MRR), theinputquantityis normalized using equation (1). For Larger the Better characteristics (MRR), equation (2) is used. These equations are takenfrom[3].

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Table3.2Detailsofinputparametersandconsideredresponsesforall25experiments[10]

Exp.No. Cutting Tool Spindle speed Feed rate Depth of cut Machining force Surface roughness “Ra

1 1 690 0.6 0.5 23.3 4.11

2 1 960 0..8 1 23.5 4.07

3 1 1153 1 1.5 24.6 3.86

4 1 1950 1.2 2 24.1 3.66

5 1 2500 1.4 2.5 23.4 3.24

6 2 690 0.8 1.5 23.4 3.18

7 2 960 1 2 22.9 3.86

8 2 1153 1.2 2.5 23.5 3.26

9 2 1950 1.4 0.5 23.6 3.51

10 2 2500 0.6 1 23.3 3.13

11 3 690 1 2.5 23.7 2.83

12 3 960 1.2 0.5 23.7 3.87

13 3 1153 1.4 1 23.2 2.65

14 3 1950 0.6 1.5 23.2 3.84

15 3 2500 0.8 2 23.5 3.25 16 4 690 1.2 1 22.7 3.14 17 4 960 1.4 1.5 23.1 3.13

18 4 1153 0.6 2 22.4 3.52 19 4 1950 0.8 2.5 22.5 3.53

20 4 2500 1 0.5 22.8 3.39 21 5 690 1.4 2 22.2 2.75 22 5 960 0.6 2.5 21.2 3.1 23 5 1153 0.8 0.5 21.6 2.97 24 5 1950 1 1 20.6 2.88 25 5 2500 1.2 1.5 21.9 2.49

Table4.3Greyrelationcoefficient

Greyrelationcoefficient

GRGMachiningforce Surfaceroughness“Ra 0.426 0.333 0.379 0.408 0.339 0.374 0.333 0.372 0.352 0.364 0.409 0.386 0.417 0.519 0.468 0.417 0.540 0.478 0.465 0.372 0.418 0.408 0.513 0.460 0.400 0.443 0.421 0.426 0.559 0.492 0.392 0.704 0.548 0.392 0.370 0.381 0.435 0.835 0.635 0.435 0.375 0.405 0.408 0.516 0.462 0.488 0.555 0.521 0.444 0.559 0.502 0.526 0.440 0.483 0.513 0.438 0.475 0.476 0.474 0.475 0.556 0.757 0.656 0.769 0.570 0.670 0.667 0.628 0.647 1.000 0.675 0.838 0.606 1.000 0.803

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Greyrelationcoefficient GRG Rank Machiningforce Surface roughness“Ra 0.426 0.333 0.379 13 0.408 0.339 0.374 15 0.333 0.372 0.352 17 0.364 0.409 0.386 14 0.417 0.519 0.468 9 0.417 0.540 0.478 7 0.465 0.372 0.418 15 0.408 0.513 0.460 14 0.400 0.443 0.421 16 0.426 0.559 0.492 9 0.392 0.704 0.548 7 0.392 0.370 0.381 19 0.435 0.835 0.635 6 0.435 0.375 0.405 16 0.408 0.516 0.462 14 0.488 0.555 0.521 8 0.444 0.559 0.502 8 0.526 0.440 0.483 9 0.513 0.438 0.475 9 0.476 0.474 0.475 10 0.556 0.757 0.656 4 0.769 0.570 0.670 3 0.667 0.628 0.647 5 1.000 0.675 0.838 1 0.606 1.000 0.803 2

Table4.6.Experimentalandpredictedvaluesofgreyrelationalgrade

CuttingTool(A) 0.379

Spindlespeed(B)

Feedrate(C)

Depthofcut(D)

Grey relational grade

Grey relational grade improvement 0.0051

Hence the grey relational analysis based on taguchi method for the optimization of the A particularly important tool is multi responseproblems.ForpredictingtheMachiningforceandsurfaceroughness

The improvement of grey relational grade from initial parameter combination (A1 B1 C1 D1 to the optimal parameter combination(A5B4C3D2)isfoundtobe0.0051

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International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

CONCLUSION

1.ThespindlespeedandfeedratehavenoeffectontheSRandMachiningforceofCFRPcompositelaminates.

2. The Grey Taguchi method can be used to simplify and improve the optimization of numerous common performance criteria.Accordingtotheresponsetable,themaximumsettingfortheGRGforspindlespeedis4000rpm,feedrateis200 mm/min,andcutdepthis0.5mm(A5B4C3D2).

3.ThedepthofcutisthemostimportantfactorinfluencingtheSRandDLF.TodeterminetheSRandDLF,cuttingdepthis acriticalparameterthatinteractsheavilywithspindlespeed.

4.MachiningforceANOVAresultItisdiscoveredthattheCuttingTool(P=0.003)(47.73percentpercent)hasthegreatest influenceonthemachiningforce.Spindlespeed(P=0.048)(18.30percent),Feedrate(0.098)(13.30percent),andDepth ofcut(0.136)(11.27percent)werethenextmostsignificant.Inthecurrentstudy,parametershaveasignificantimpacton surfaceroughness.

5. The R sq value is 91.51 percent, indicating good agreement between the input and output relationships. It demonstrates that the input and output variables have a strong relationship. Now, the R Sq (adj) value is 74.52 percent, indicatingthatthedataarewellfittedforthenewsetsofvariables.

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