机械外文翻译中英文Word文件下载.docx

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英文原文

Selectionofoptimumtoolgeometryandcuttingconditions

usingasurfaceroughnesspredictionmodelforendmilling

AbstractInfluenceoftoolgeometryonthequalityofsurfaceproducediswellknownandhenceanyattempttoassesstheperformanceofendmillingshouldincludethetoolgeometry.Inthepresentwork,experimentalstudieshavebeenconductedtoseetheeffectoftoolgeometry（radialrakeangleandnoseradius）andcuttingconditions（cuttingspeedandfeedrate）onthemachiningperformanceduringendmillingofmediumcarbonsteel.Thefirstandsecondordermathematicalmodels,intermsofmachiningparameters,weredevelopedforsurfaceroughnesspredictionusingresponsesurfacemethodology（RSM）onthebasisofexperimentalresults.ThemodelselectedforoptimizationhasbeenvalidatedwiththeChisquaretest.Thesignificanceoftheseparametersonsurfaceroughnesshasbeenestablishedwithanalysisofvariance.Anattempthasalsobeenmadetooptimizethesurfaceroughnesspredictionmodelusinggeneticalgorithms（GA）.TheGAprogramgivesminimumvaluesofsurfaceroughnessandtheirrespectiveoptimalconditions.

1Introduction

Endmillingisoneofthemostcommonlyusedmetalremovaloperationsinindustrybecauseofitsabilitytoremovematerialfastergivingreasonablygoodsurfacequality.Itisusedinavarietyofmanufacturingindustriesincludingaerospaceandautomotivesectors,wherequalityisanimportantfactorintheproductionofslots,pockets,precisionmouldsanddies.Greaterattentionisgiventodimensionalaccuracyandsurfaceroughnessofproductsbytheindustrythesedays.Moreover,surfacefinishinfluencesmechanicalpropertiessuchasfatiguebehaviour,wear,corrosion,lubricationandelectricalconductivity.Thus,measuringandcharacterizingsurfacefinishcanbeconsideredforpredictingmachiningperformance.

Surfacefinishresultingfromturningoperationshastraditionallyreceivedconsiderableresearchattention,whereasthatofmachiningprocessesusingmultipointcutters,requiresattentionbyresearchers.Astheseprocessesinvolvelargenumberofparameters,itwouldbedifficulttocorrelatesurfacefinishwithotherparametersjustbyconductingexperiments.Modellinghelpstounderstandthiskindofprocessbetter.Thoughsomeamountofworkhasbeencarriedouttodevelopsurfacefinishpredictionmodelsinthepast,theeffectoftoolgeometryhasreceivedlittleattention.However,theradialrakeanglehasamajoraffectonthepowerconsumptionapartfromtangentialandradialforces.Italsoinfluenceschipcurlingandmodifieschipflowdirection.Inadditiontothis,researchers[1]havealsoobservedthatthenoseradiusplaysasignificantroleinaffectingthesurfacefinish.Thereforethedevelopmentofagoodmodelshouldinvolvetheradialrakeangleandnoseradiusalongwithotherrelevantfactors.

Establishmentofefficientmachiningparametershasbeenaproblemthathasconfrontedmanufacturingindustriesfornearlyacentury,andisstillthesubjectofmanystudies.Obtainingoptimummachiningparametersisofgreatconcerninmanufacturingindustries,wheretheeconomyofmachiningoperationplaysakeyroleinthecompetitivemarket.Inmaterialremovalprocesses,animproperselectionofcuttingconditionscausesurfaceswithhighroughnessanddimensionalerrors,anditisevenpossiblethatdynamicphenomenaduetoautoexcitedvibrationsmaysetin[2].Inviewofthesignificantrolethatthemillingoperationplaysintoday’smanufacturingworld,thereisaneedtooptimizethemachiningparametersforthisoperation.So,anefforthasbeenmadeinthispapertoseetheinfluenceoftoolgeometry（radialrakeangleandnoseradius）andcuttingconditions（cuttingspeedandfeedrate）onthesurfacefinishproducedduringendmillingofmediumcarbonsteel.Theexperimentalresultsofthisworkwillbeusedtorelatecuttingspeed,feedrate,radialrakeangleandnoseradiuswiththemachiningresponsei.e.surfaceroughnessbymodelling.Themathematicalmodelsthusdevelopedarefurtherutilizedtofindtheoptimumprocessparametersusinggeneticalgorithms.

2Review

Processmodellingandoptimizationaretwoimportantissuesinmanufacturing.Themanufacturingprocessesarecharacterizedbyamultiplicityofdynamicallyinteractingprocessvariables.Surfacefinishhasbeenanimportantfactorofmachininginpredictingperformanceofanymachiningoperation.Inordertodevelopandoptimizeasurfaceroughnessmodel,itisessentialtounderstandthecurrentstatusofworkinthisarea.

Davisetal.[3]haveinvestigatedthecuttingperformanceoffiveendmillshavingvarioushelixangles.CuttingtestswereperformedonaluminiumalloyL65forthreemillingprocesses（face,slotandside）,inwhichcuttingforce,surfaceroughnessandconcavityofamachinedplanesurfaceweremeasured.Thecentralcompositedesignwasusedtodecideonthenumberofexperimentstobeconducted.Thecuttingperformanceoftheendmillswasassessedusingvarianceanalysis.Theaffectsofspindlespeed,depthofcutandfeedrateonthecuttingforceandsurfaceroughnesswerestudied.Theinvestigationshowedthatendmillswithlefthandhelixanglesaregenerallylesscosteffectivethanthosewithrighthandhelixangles.Thereisnosignificantdifferencebetweenupmillinganddownmillingwithregardtothecuttingforce,althoughthedifferencebetweenthemregardingthesurfaceroughnesswaslarge.Bayoumietal.[4]havestudiedtheaffectofthetoolrotationangle,feedrateandcuttingspeedonthemechanisticprocessparameters（pressure,frictionparameter）forendmillingoperationwiththreecommerciallyavailableworkpiecematerials,11L17freemachiningsteel,62-35-3freemachiningbrassand2024aluminiumusingasingleflutedHSSmillingcutter.Ithasbeenfoundthatpressureandfrictionactonthechip–toolinterfacedecreasewiththeincreaseoffeedrateandwiththedecreaseoftheflowangle,whilethecuttingspeedhasanegligibleeffectonsomeofthematerialdependentparameters.Processparametersaresummarizedintoempiricalequationsasfunctionsoffeedrateandtoolrotationangleforeachworkmaterial.However,researchershavenottakenintoaccounttheeffectsofcuttingconditionsandtoolgeometrysimultaneously;

besidesthesestudieshavenotconsideredtheoptimizationofthecuttingprocess.

Asendmillingisaprocesswhichinvolvesalargenumberfparameters,combinedinfluenceofthesignificantparametersanonlybeobtainedbymodelling.MansourandAbdallaetal.[5]havedevelopedasurfaceroughnessmodelfortheendmillingofEN32M（asemi-freecuttingcarboncasehardeningsteelwithimprovedmerchantability）.Themathematicalmodelhasbeendevelopedintermsofcuttingspeed,feedrateandaxialdepthofcut.Theaffectoftheseparametersonthesurfaceroughnesshasbeencarriedoutusingresponsesurfacemethodology（RSM）.Afirstorderequationcoveringthespeedrangeof30–35m/minandasecondorderequationcoveringthespeedrangeof24–38m/minweredevelopedunderdrymachiningconditions.Alauddinetal.[6]developedasurfaceroughnessmodelusingRSMfortheendmillingof190BHNsteel.Firstandsecondordermodelswereconstructedalongwithcontourgraphsfortheselectionofthepropercombinationofcuttingspeedandfeedtoincreasethemetalremovalratewithoutsacrificingsurfacequality.Hasmietal.[7]alsousedtheRSMmodelforassessingtheinfluenceoftheworkpiecematerialonthesurfaceroughnessofthemachinedsurfaces.Themodelwasdevelopedformillingoperationbyconductingexperimentsonsteelspecimens.Theexpressionshows,therelationshipbetweenthesurfaceroughnessandthevariousparameters;

namely,thecuttingspeed,feedanddepthofcut.Theabovemodelshavenotconsideredtheaffectoftoolgeometryonsurfaceroughness.

Sincetheturnofthecenturyquitealargenumberofattemptshavebeenmadetofindoptimumvaluesofmachiningparameters.Usesofmanymethodshavebeenreportedintheliteraturetosolveoptimizationproblemsformachiningparameters.JainandJain[8]haveusedneuralnetworksformodelingandoptimizingthemachiningconditions.Theresultshavebeenvalidatedbycomparingtheoptimizedmachiningconditionsobtainedusinggeneticalgorithms.Sureshetal.[9]havedevelopedasurfaceroughnesspredictionmodelforturningmildsteelusingaresponsesurfacemethodologytoproducethefactoraffectsoftheindividualprocessparameters.Theyhavealsooptimizedtheturningprocessusingthesurfaceroughnesspredictionmodelastheobjectivefunction.Consideringtheabove,anattempthasbeenmadeinthisworktodevelopasurfaceroughnessmodelwithtoolgeometryandcuttingconditionsonthebasisofexperimentalresultsandthenoptimizeitfortheselectionoftheseparameterswithinthegivenconstraintsintheendmillingoperation.

3Methodology

Inthiswork,mathematicalmodelshavebeendevelopedusingexperimentalresultswiththehelpofresponsesurfacemethodology.Thepurposeofdevelopingmathematicalmodelsrelatingthemachiningresponsesandtheirfactorsistofacilitatetheoptimizationofthemachiningprocess.Thismathematicalmodelhasbeenusedasanobjectivefunctionandtheoptimizationwascarriedoutwiththehelpofgeneticalgorithms.

3.1Mathematicalformulation

Responsesurfacemethodology（RSM）isacombinationofmathematicalandstatisticaltechniquesusefulformodellingandanalyzingtheproblemsinwhichseveralindependentvariablesinfluenceadependentvariableorresponse.Themathematicalmodelscommonlyusedarerepresentedby:

whereYisthemachiningresponse,ϕistherespons