机械外文翻译中英文Word文档下载推荐.docx
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机械外文翻译中英文Word文档下载推荐.docx
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usingasurfaceroughnesspredictionmodelforendmilling
Abstract:
Influenceoftoolgeometryonthequalityofsurfaceproducediswellknownandhenceanyattempttoassesstheperformanceofendmillingshouldincludethetoolgeometry.Inthepresentwork,experimentalstudieshavebeenconductedtoseetheeffectoftoolgeometry(radialrakeangleandnoseradius)andcuttingconditions(cuttingspeedandfeedrate)onthemachiningperformanceduringendmillingofmediumcarbonsteel.Thefirstandsecondordermathematicalmodels,intermsofmachiningparameters,weredevelopedforsurfaceroughnesspredictionusingresponsesurfacemethodology(RSM)onthebasisofexperimentalresults.ThemodelselectedforoptimizationhasbeenvalidatedwiththeChisquaretest.Thesignificanceoftheseparametersonsurfaceroughnesshasbeenestablishedwithanalysisofvariance.Anattempthasalsobeenmadetooptimizethesurfaceroughnesspredictionmodelusinggeneticalgorithms(GA).TheGAprogramgivesminimumvaluesofsurfaceroughnessandtheirrespectiveoptimalconditions.
1、Introduction
Endmillingisoneofthemostcommonlyusedmetalremovaloperationsinindustrybecauseofitsabilitytoremovematerialfastergivingreasonablygoodsurfacequality.Itisusedinavarietyofmanufacturingindustriesincludingaerospaceandautomotivesectors,wherequalityisanimportantfactorintheproductionofslots,pockets,precisionanddies.Greaterattentionisgiventodimensionalaccuracyandsurfaceroughnessofproductsbytheindustrythesedays.Moreover,surfacefinishinfluencesmechanicalpropertiessuchasfatiguebehaviour,wear,corrosion,lubricationandelectricalconductivity.Thus,measuringandcharacterizingsurfacefinishcanbeconsideredforpredictingmachiningperformance.
Surfacefinishresultingfromturningoperationshastraditionallyreceivedconsiderableresearchattention,whereasthatofmachiningprocessesusingcutters,requiresattentionbyresearchers.Astheseprocessesinvolvelargenumberofparameters,itwouldbedifficulttocorrelatesurfacefinishwithotherparametersjustbyconductingexperiments.Modelinghelpstounderstandthiskindofprocessbetter.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.surfaceroughnessbymodeling.Themathematicalmodelsthusdevelopedarefurtherutilizedtofindtheoptimumprocessparametersusinggeneticalgorithms.
2、Review
Processmodelingandoptimizationaretwoimportantissuesinmanufacturing.Themanufacturingprocessesarecharacterizedbyamultiplicityofdynamicallyinteractingprocessvariables.Surfacefinishhasbeenanimportantfactorofmachininginpredictingperformanceofanymachiningoperation.Inordertodevelopandoptimizeasurfaceroughnessmodel,itisessentialtounderstandthecurrentstatusofworkinthisarea.
Davisetal.[3]haveinvestigatedthecuttingperformanceoffiveendmillshavingvarioushelixangles.CuttingtestswereperformedonalloyL65forthreemillingprocesses(face,slotandside),inwhichcuttingforce,surfaceroughnessandconcavityofamachinedplanesurfaceweremeasured.Thecentralcompositedesignwasusedtodecideonthenumberofexperimentstobeconducted.Thecuttingperformanceoftheendmillswasassessedusingvarianceanalysis.Theaffectsofspindlespeed,depthofcutandfeedrateonthecuttingforceandsurfaceroughnesswerestudied.Theinvestigationshowedthatendmillswithlefthandhelixanglesaregenerallylesscosteffectivethanthosewithrighthandhelixangles.Thereisnosignificantdifferencebetweenupmillinganddownmillingwithregardtothecuttingforce,althoughthedifferencebetweenthemregardingthesurfaceroughnesswaslarge.[4]havestudiedtheaffectofthetoolrotationangle,feedrateandcuttingspeedonthemechanisticprocessparameters(pressure,frictionparameter)forendmillingoperationwiththreecommerciallyavailableworkpiecematerials,11L17freemachiningsteel,62-35-3freemachiningbrassand2024usingasingleflutedHSSmillingcutter.Ithasbeenfoundthatpressureandfrictionactonthechip–toolinterfacedecreasewiththeincreaseoffeedrateandwiththedecreaseoftheflowangle,whilethecuttingspeedhasanegligibleeffectonsomeofthematerialdependentparameters.Processparametersaresummarizedintoempiricalequationsasfunctionsoffeedrateandtoolrotationangleforeachworkmaterial.However,researchershavenottakenintoaccounttheeffectsofcuttingconditionsandtoolgeometrysimultaneously;
besidesthesestudieshavenotconsideredtheoptimizationofthecuttingprocess.
Asendmillingisaprocesswhichinvolvesalargenumberfparameters,combinedinfluenceofthesignificantparametersanonlybeobtainedbymodeling.[5]havedevelopedasurfaceroughnessmodelfortheendmillingofEN32M(asemi-freecuttingcarboncasehardeningsteelwithimprovedmerchantability).Themathematicalmodelhasbeendevelopedintermsofcuttingspeed,feedrateandaxialdepthofcut.Theaffectoftheseparametersonthesurfaceroughnesshasbeencarriedoutusingresponsesurfacemethodology(RSM).Afirstorderequationcoveringthespeedrangeof30–35m/minandasecondorderequationcoveringthespeedrangeof24–38m/minweredevelopedunderdrymachiningconditions.[6]developedasurfaceroughnessmodelusingRSMfortheendmillingof190BHNsteel.Firstandsecondordermodelswereconstructedalongwithcontourgraphsfortheselectionofthepropercombinationofcuttingspeedandfeedtoincreasethemetalremovalratewithoutsacrificingsurfacequality.[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,ϕistheresponsefunctionandS,f,α,raremillingvariablesand∈istheerrorwhichisnormallydistributedabouttheobservedrespon
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