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外文翻译--端铣削自适应切削力的模糊控制策略 英文版.pdf外文翻译--端铣削自适应切削力的模糊控制策略 英文版.pdf -- 5 元

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JournalofMaterialsProcessingTechnologyxxx2005xxx–xxxAbstractoperations.metalcuttingforfeedrateconducted©K1.theofaremeragearederEvanthemachiningnecessarysatisfytioperatingAtainconditions.de09240136/doi10.1016/j.jmatprotec.2005.02.143FuzzycontrolstrategyforanadaptiveforcecontrolinendmillingU.Zuperl∗,F.Cus,M.MilfelnerFacultyofMechanicalEngineering,UniversityofMaribor,Smetanova17,2000Maribor,SloveniaThispaperdiscussestheapplicationoffuzzyadaptivecontrolstrategytotheproblemofcuttingforcecontrolinhighspeedendmillingTheresearchisconcernedwithintegratingadaptivecontrolwithastandardcomputernumericalcontrollerCNCforoptimisingaprocess.Itisdesignedtoadaptivelymaximisethefeedratesubjecttoallowablecuttingforceonthetool,whichisverybeneficialatimeconsumingcomplexshapemachining.Thepurposeistopresentareliable,robustneuralcontrolleraimedatadaptivelyadjustingtopreventexcessivetoolwear,toolbreakageandmaintainahighchipremovalrate.Numeroussimulationsandexperimentsaretoconfirmtheefficiencyofthisarchitecture.2005ElsevierB.V.Allrightsreserved.eywordsEndmillingAdaptiveforcecontrolFuzzyIntroductionAremainingdrawbackofmodernCNCsystemsisthatmachiningparameters,suchasfeedrate,speedanddepthcut,areprogrammedoffline.Themachiningparameterssimulationswiththefuzzycontrolstrategyarecarriedout.Theresultsdemonstratetheabilityoftheproposedsystemtoeffectivelyregulatepeakforcesforcuttingconditionscommonlyencounteredinendmillingoperations.Forcecontrolalgorithmshavebeendevelopedandevalusuallyselectedbeforemachiningaccordingtoprogramsexperienceandmachininghandbooks.Topreventdamandtoavoidmachiningfailuretheoperatingconditionsusuallysetextremelyconservative.Asaresult,manyCNCsystemsareinefficientandrununtheoperatingconditionsthatarefarfromoptimalcriteria.enifthemachiningparametersareoptimisedofflinebyoptimisationalgorithm5theycannotbeadjustedduringmachiningprocess.Toensurethequalityofmachiningproducts,toreducethecostsandincreasethemachiningefficiency,itistoadjustthemachiningparametersinrealtime,totheoptimalmachiningcriteria.Forthisreason,adapvecontrolAC,whichprovidesonlineadjustmentoftheconditions,isbeingstudiedwithinterest3.InourCsystem,thefeedrateisadjustedonlineinordertomainaconstantcuttingforceinspiteofvariationsincuttingInthispaper,asimplefuzzycontrolstrategyisvelopedintheintelligentsystemandsomeexperimental∗Correspondingauthor.Tel.38622207623fax38622207990.Emailaddressuros.zuperlunimb.siU.Zuperl.uatedisnallyantrollerditions.trollerandatedallthetems,bysentedtesystems3controlhasmotion.for–seefrontmatter©2005ElsevierB.V.Allrightsreserved.bynumerousresearchers.AmongthemostcommonthefixedgainproportionalintegralPIcontrollerorigiproposedformillingby4.Kimetal.4proposedadjustablegainPIcontrollerwherethegainoftheconisadjustedinresponsetovariationsincuttingconThepurelyadaptivemodelreferenceadaptiveconMRACapproachwasoriginallyinvestigatedbyCusBalic2.Thesecontrollersweresimulatedandevaluandphysicallyimplementedby1.BothstudiesfoundthreeparameteradaptivecontrollertoperformbetterthanfixedgainPIcontroller.AsregardsfuzzycontrolsysanintroductorysurveyofpioneeringactivitiesisgivenHuangandLin3,andamoresystematicviewisprebyin4.ComparisonsoffuzzywithproportionalingralderivativePIDcontrolandstabilityanalysisoffuzzyandsupervisoryfuzzycontrolareaddressedinRef..Muchworkhasbeendoneontheadaptivecuttingforceformilling2.However,mostofthepreviousworksimplifiedtheproblemofmillingintoonedimensionalInthiscontribution,wewillconsiderforcecontrolthreedimensionalmilling.2Processingscribesthesimulation/eposedimentalresearch.2.fuzzyseteThewhichplementcontrolmoreTherateasthecomparedcontrolFuzzyratecuttingcreasesrates,productionarebreakage.callytheforbelo1.2.3.4.signingonactualcentagemisationalcorrectcontrolleraplepro2.1.aaboutinputoperatorthroughU.Zuperletal./JournalofMaterialsThepaperisorganisedasfollows.Section2brieflydetheoverallforcecontrolstrategy.Section3coversCNCmachiningprocessmodel.Section5describesthexperimentsandimplementationmethodofprocontrolscheme.Finally,Sections6and7presentexperresults,conclusions,andrecommendationsforfutureAdaptivefuzzycontrollerstructureAnewonlinecontrolschemewhichiscalledadaptivecontrolAFCFig.1isdevelopedbyusingthefuzzytheory.Thebasicideaofthisapproachistoincorporatethexperienceofahumanoperatorindesignofthecontroller.controlstrategiesareformulatedasanumberofrulesaresimpletocarryoutmanuallybutdifficulttoimbyusingconventionalalgorithm.Basedonthisnewstrategy,verycomplicatedprocesscanbecontrolledeasilyandaccuratelycomparedtostandardapproaches.objectiveoffuzzycontroliskeepingthemetalremovalMRRashighaspossibleandmaintainingcuttingforcecloseaspossibletoagivenreferencevalue.Furthermore,amountofcomputationtaskandtimecanbereducedastoclassicalormoderncontroltheory.Schematicrulesareconstructedbyusingrealexperimentaldata.adaptivecontrolensurescontinuousoptimisingfeedcontrolthatisautomaticallyadjustedtoeachparticularsituation.Whenspindleloadsarelow,thesystemincuttingfeedsaboveandbeyondpreprogrammedfeedresultinginconsiderablereductionsincycletimesandcosts.Whenspindleloadsarehighthefeedrateslowered,safeguardingmachinetoolsfromdamagefromWhensystemdetectsextremeforces,itautomatistopsthemachinetoprotectthecuttingtool.Itreducesneedforconstantoperatorsupervision.Sequenceofstepsonlineoptimisationofthemillingprocessarepresentedw.namicstheasvofcuttinglated,Delta1forceFig.1.ComparisonofactualTechnologyxxx2005xxx–xxxThepreprogrammedfeedratesaresenttoCNCcontrollerofthemillingmachine.Themeasuredcuttingforcesaresenttothefuzzycontroller.Fuzzycontrollerusestheenteredrulestofindadjusttheoptimalfeedratesandsendsitbacktothemachine.Steps1and3arerepeateduntilterminationofmachining.TheadaptiveforcecontrolleradjuststhefeedratebyasafeedrateoverridepercentagetotheCNCcontrollerafouraxisHeller,basedonameasuredpeakforce.Thefeedrateistheproductofthefeedrateoverrideperandtheprogrammedfeedrate.Ifthefeedrateoptimodelswereperfect,theoptimisedfeedratewouldwaysbeequaltothereferencepeakforce.Inthiscasetheoverridepercentagewouldbe100.Inorderforthetoregulatepeakforce,forceinformationmustbevailabletothecontrolalgorithmateverycontrollersamtime.AdataacquisitionsoftwareLabviewisusedtovidethisinformation.StructureofafuzzycontrollerInfuzzyprocesscontrol,expertiseisencapsulatedintosystemintermsoflinguisticdescriptionsofknowledgehumanoperatingcriteria,andknowledgeaboutthe±outputrelationships.Thealgorithmisbasedonthesknowledge,butitalsoincludescontroltheory,theerrorderivative,takingintoconsiderationthedyoftheprocess.Thus,thecontrollerhasasitsinputs,cuttingforceerrorDelta1FanditsfirstdifferenceDelta12F,andoutputs,thevariationinfeedrateDelta1f.ThefuzzycontrolariablesfuzzificationseeFig.2aswellasthecreationtherulesbaseweretakenfromtheexpertoperator.Theforceerrorandfirstdifferenceoftheerrorarecalcuateachsamplinginstantk,asDelta1FkFref−Fkand2FkDelta1Fk−Delta1Fk−1,whereFismeasuredcuttingandFrefisforcesetpoint.andmodelfeedrate.3.etalandforcesscribedmachinefeedingfitquencefromformcommandedtingmodel.mentalfeedrateU.Zuperletal./JournalofMaterialsProcessingFig.2.StructureofafuzzyCNCmachiningprocessmodelACNCmachiningprocessmodelsimulatorisusedtovaluatethecontrollerdesignbeforeconductingexperimentests.Theprocessmodelconsistsofaneuralforcemodelfeeddrivemodel.TheneuralmodelestimatescuttingbasedoncuttingconditionsandcutgeometryasdebyZuperl1.Thefeeddrivemodelsimulatestheresponsetochangesincommandedfeedrate.Thedrivemodelwasdeterminedexperimentallybyexaminstepchangesinthecommandedvelocity.Thebestmodelwasfoundtobeasecondordersystemwithanaturalfreyof3Hzandasettlingtimeof0.4s.Comparisonofxperimentalandsimulationresultsofavelocitystepchange7to22mm/sisshownonFig.3.ThefeeddriveandneuralforcemodelarecombinedtotheCNCmachiningprocessmodel.ModelinputisthefeedrateandtheoutputistheX,Yresultantcutforce.ThecutgeometryisdefinedintheneuralforceThesimulatorisverifiedbycomparisonofexperiandmodelsimulationresults.Avarietyofcutswithchangesweremadeforvalidation.changeFig.resultsTechnologyxxx2005xxx–xxx3controller.Theexperimentalandsimulationresultantforceforastepinfeedratefrom0.05to2mm/toothispresentedin4.Theexperimentalresultscorrelatewellwithmodelintermsofaverageandpeakforce.TheexperimentalFig.3.Comparisonofactualandmodelfederate.4resultsandthe3.1.dardlarimentsforceusedfederatedialforcesaryU.Zuperletal./JournalofMaterialsProcessingFig.4.Structureofafuzzycorrelatewellwithmodelresultsintermsofaveragepeakforce.Theobviousdiscrepancymaybeduetoinaccuraciesinneuralmodel,andunmodeledsystemdynamics.CuttingforcemodelingTorealisetheonlinemodellingofcuttingforces,astanBPneuralnetworkNNisproposedbasedonthepopubackpropagationleeringrule.Duringpreliminaryexperitprovedtobesufficientlycapableofextractingthemodeldirectlyfromexperimentalmachiningdata.Itistosimulatethecuttingprocess.TheNNformodellingneedsfourinputneuronsformillingf,cuttingspeedvcaxialdepthofcutADandradepthofcutRD.TheoutputfromtheNNarecuttingcomponents,thereforetwooutputneuronsareneces.ThedetailedtopologyoftheusedNNwithoptimaltrainingalso73.2.modelingferentberefnetwysed.difandtheinputconclusionsTechnologyxxx2005xxx–xxxcontroller.parametersandmathematicalprincipleoftheneuronisshowninFig.5.BestNNconfigurationcontains5,3andhiddenneuronsinhiddenlayers.TopologyofneuralnetworkanditsadaptationtoproblemTheeffectoftopologyisalsostudiedbyconsideringdifcases.Thetopologiesarevariedbyvaryingthenumofneuronsinhiddenlayers.Toevaluatetheindividualfectsoftrainingparametersontheperformanceofneuralork40differentnetworksweretrained,testedandanalThenetworkperformanceswereevaluatedusingfourferentcriteria5ETstMax,ETst,ETrn,andETrnMaxthenumberoftrainingcycles.Thenumberofneuronsininputandoutputlayersaredeterminedbythenumberofandoutputparameters.Fromtheresultsthefollowingcanbedrawn.
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