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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

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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

JOURNALOFMATERIALSPROCESSINGTECHNOLOGYXXX2005XXX–XXXABSTRACTOPERATIONSMETALCUTTINGFORFEEDRATECONDUCTEDK1THEOFAREMER’AGEAREDEREVANTHEMACHININGNECESSARYSATISFYTIOPERATINGATAINCONDITIONSDE09240136/DOI101016/JJMATPROTEC200502143FUZZYCONTROLSTRATEGYFORANADAPTIVEFORCECONTROLINENDMILLINGUZUPERL∗,FCUS,MMILFELNERFACULTYOFMECHANICALENGINEERING,UNIVERSITYOFMARIBOR,SMETANOVA17,2000MARIBOR,SLOVENIATHISPAPERDISCUSSESTHEAPPLICATIONOFFUZZYADAPTIVECONTROLSTRATEGYTOTHEPROBLEMOFCUTTINGFORCECONTROLINHIGHSPEEDENDMILLINGTHERESEARCHISCONCERNEDWITHINTEGRATINGADAPTIVECONTROLWITHASTANDARDCOMPUTERNUMERICALCONTROLLERCNCFOROPTIMISINGAPROCESSITISDESIGNEDTOADAPTIVELYMAXIMISETHEFEEDRATESUBJECTTOALLOWABLECUTTINGFORCEONTHETOOL,WHICHISVERYBENEFICIALATIMECONSUMINGCOMPLEXSHAPEMACHININGTHEPURPOSEISTOPRESENTARELIABLE,ROBUSTNEURALCONTROLLERAIMEDATADAPTIVELYADJUSTINGTOPREVENTEXCESSIVETOOLWEAR,TOOLBREAKAGEANDMAINTAINAHIGHCHIPREMOVALRATENUMEROUSSIMULATIONSANDEXPERIMENTSARETOCONFIRMTHEEFFICIENCYOFTHISARCHITECTURE2005ELSEVIERBVALLRIGHTSRESERVEDEYWORDSENDMILLING;ADAPTIVEFORCECONTROL;FUZZYINTRODUCTIONAREMAININGDRAWBACKOFMODERNCNCSYSTEMSISTHATMACHININGPARAMETERS,SUCHASFEEDRATE,SPEEDANDDEPTHCUT,AREPROGRAMMEDOFFLINETHEMACHININGPARAMETERSSIMULATIONSWITHTHEFUZZYCONTROLSTRATEGYARECARRIEDOUTTHERESULTSDEMONSTRATETHEABILITYOFTHEPROPOSEDSYSTEMTOEFFECTIVELYREGULATEPEAKFORCESFORCUTTINGCONDITIONSCOMMONLYENCOUNTEREDINENDMILLINGOPERATIONSFORCECONTROLALGORITHMSHAVEBEENDEVELOPEDANDEVALUSUALLYSELECTEDBEFOREMACHININGACCORDINGTOPROGRAMSEXPERIENCEANDMACHININGHANDBOOKSTOPREVENTDAMANDTOAVOIDMACHININGFAILURETHEOPERATINGCONDITIONSUSUALLYSETEXTREMELYCONSERVATIVEASARESULT,MANYCNCSYSTEMSAREINEFFICIENTANDRUNUNTHEOPERATINGCONDITIONSTHATAREFARFROMOPTIMALCRITERIAENIFTHEMACHININGPARAMETERSAREOPTIMISEDOFFLINEBYOPTIMISATIONALGORITHM5THEYCANNOTBEADJUSTEDDURINGMACHININGPROCESSTOENSURETHEQUALITYOFMACHININGPRODUCTS,TOREDUCETHECOSTSANDINCREASETHEMACHININGEFFICIENCY,ITISTOADJUSTTHEMACHININGPARAMETERSINREALTIME,TOTHEOPTIMALMACHININGCRITERIAFORTHISREASON,ADAPVECONTROLAC,WHICHPROVIDESONLINEADJUSTMENTOFTHECONDITIONS,ISBEINGSTUDIEDWITHINTEREST3INOURCSYSTEM,THEFEEDRATEISADJUSTEDONLINEINORDERTOMAINACONSTANTCUTTINGFORCEINSPITEOFVARIATIONSINCUTTINGINTHISPAPER,ASIMPLEFUZZYCONTROLSTRATEGYISVELOPEDINTHEINTELLIGENTSYSTEMANDSOMEEXPERIMENTAL∗CORRESPONDINGAUTHORTEL38622207623;FAX38622207990EMAILADDRESSUROSZUPERLUNIMBSIUZUPERLUATEDISNALLYANTROLLERDITIONSTROLLERANDATEDALLTHETEMS,BYSENTEDTESYSTEMS3CONTROLHASMOTIONFOR–SEEFRONTMATTER2005ELSEVIERBVALLRIGHTSRESERVEDBYNUMEROUSRESEARCHERSAMONGTHEMOSTCOMMONTHEFIXEDGAINPROPORTIONALINTEGRALPICONTROLLERORIGIPROPOSEDFORMILLINGBY4KIMETAL4PROPOSEDADJUSTABLEGAINPICONTROLLERWHERETHEGAINOFTHECONISADJUSTEDINRESPONSETOVARIATIONSINCUTTINGCONTHEPURELYADAPTIVEMODELREFERENCEADAPTIVECONMRACAPPROACHWASORIGINALLYINVESTIGATEDBYCUSBALIC2THESECONTROLLERSWERESIMULATEDANDEVALUANDPHYSICALLYIMPLEMENTEDBY1BOTHSTUDIESFOUNDTHREEPARAMETERADAPTIVECONTROLLERTOPERFORMBETTERTHANFIXEDGAINPICONTROLLERASREGARDSFUZZYCONTROLSYSANINTRODUCTORYSURVEYOFPIONEERINGACTIVITIESISGIVENHUANGANDLIN3,ANDAMORESYSTEMATICVIEWISPREBYIN4COMPARISONSOFFUZZYWITHPROPORTIONALINGRALDERIVATIVEPIDCONTROLANDSTABILITYANALYSISOFFUZZYANDSUPERVISORYFUZZYCONTROLAREADDRESSEDINREFMUCHWORKHASBEENDONEONTHEADAPTIVECUTTINGFORCEFORMILLING2HOWEVER,MOSTOFTHEPREVIOUSWORKSIMPLIFIEDTHEPROBLEMOFMILLINGINTOONEDIMENSIONALINTHISCONTRIBUTION,WEWILLCONSIDERFORCECONTROLTHREEDIMENSIONALMILLING2PROCESSINGSCRIBESTHESIMULATION/EPOSEDIMENTALRESEARCH2FUZZYSETETHEWHICHPLEMENTCONTROLMORETHERATEASTHECOMPAREDCONTROLFUZZYRATECUTTINGCREASESRATES,PRODUCTIONAREBREAKAGECALLYTHEFORBELO1234SIGNINGONACTUALCENTAGEMISATIONALCORRECTCONTROLLERAPLEPRO21AABOUTINPUTOPERATOR’THROUGHUZUPERLETAL/JOURNALOFMATERIALSTHEPAPERISORGANISEDASFOLLOWSSECTION2BRIEFLYDETHEOVERALLFORCECONTROLSTRATEGYSECTION3COVERSCNCMACHININGPROCESSMODELSECTION5DESCRIBESTHEXPERIMENTSANDIMPLEMENTATIONMETHODOFPROCONTROLSCHEMEFINALLY,SECTIONS6AND7PRESENTEXPERRESULTS,CONCLUSIONS,ANDRECOMMENDATIONSFORFUTUREADAPTIVEFUZZYCONTROLLERSTRUCTUREANEWONLINECONTROLSCHEMEWHICHISCALLEDADAPTIVECONTROLAFCFIG1ISDEVELOPEDBYUSINGTHEFUZZYTHEORYTHEBASICIDEAOFTHISAPPROACHISTOINCORPORATETHEXPERIENCEOFAHUMANOPERATORINDESIGNOFTHECONTROLLERCONTROLSTRATEGIESAREFORMULATEDASANUMBEROFRULESARESIMPLETOCARRYOUTMANUALLYBUTDIFFICULTTOIMBYUSINGCONVENTIONALALGORITHMBASEDONTHISNEWSTRATEGY,VERYCOMPLICATEDPROCESSCANBECONTROLLEDEASILYANDACCURATELYCOMPAREDTOSTANDARDAPPROACHESOBJECTIVEOFFUZZYCONTROLISKEEPINGTHEMETALREMOVALMRRASHIGHASPOSSIBLEANDMAINTAININGCUTTINGFORCECLOSEASPOSSIBLETOAGIVENREFERENCEVALUEFURTHERMORE,AMOUNTOFCOMPUTATIONTASKANDTIMECANBEREDUCEDASTOCLASSICALORMODERNCONTROLTHEORYSCHEMATICRULESARECONSTRUCTEDBYUSINGREALEXPERIMENTALDATAADAPTIVECONTROLENSURESCONTINUOUSOPTIMISINGFEEDCONTROLTHATISAUTOMATICALLYADJUSTEDTOEACHPARTICULARSITUATIONWHENSPINDLELOADSARELOW,THESYSTEMINCUTTINGFEEDSABOVEANDBEYONDPREPROGRAMMEDFEEDRESULTINGINCONSIDERABLEREDUCTIONSINCYCLETIMESANDCOSTSWHENSPINDLELOADSAREHIGHTHEFEEDRATESLOWERED,SAFEGUARDINGMACHINETOOLSFROMDAMAGEFROMWHENSYSTEMDETECTSEXTREMEFORCES,ITAUTOMATISTOPSTHEMACHINETOPROTECTTHECUTTINGTOOLITREDUCESNEEDFORCONSTANTOPERATORSUPERVISIONSEQUENCEOFSTEPSONLINEOPTIMISATIONOFTHEMILLINGPROCESSAREPRESENTEDWNAMICSTHEASVOFCUTTINGLATED,DELTA1FORCEFIG1COMPARISONOFACTUALTECHNOLOGYXXX2005XXX–XXXTHEPREPROGRAMMEDFEEDRATESARESENTTOCNCCONTROLLEROFTHEMILLINGMACHINETHEMEASUREDCUTTINGFORCESARESENTTOTHEFUZZYCONTROLLERFUZZYCONTROLLERUSESTHEENTEREDRULESTOFINDADJUSTTHEOPTIMALFEEDRATESANDSENDSITBACKTOTHEMACHINESTEPS1AND3AREREPEATEDUNTILTERMINATIONOFMACHININGTHEADAPTIVEFORCECONTROLLERADJUSTSTHEFEEDRATEBYASAFEEDRATEOVERRIDEPERCENTAGETOTHECNCCONTROLLERAFOURAXISHELLER,BASEDONAMEASUREDPEAKFORCETHEFEEDRATEISTHEPRODUCTOFTHEFEEDRATEOVERRIDEPERANDTHEPROGRAMMEDFEEDRATEIFTHEFEEDRATEOPTIMODELSWEREPERFECT,THEOPTIMISEDFEEDRATEWOULDWAYSBEEQUALTOTHEREFERENCEPEAKFORCEINTHISCASETHEOVERRIDEPERCENTAGEWOULDBE100INORDERFORTHETOREGULATEPEAKFORCE,FORCEINFORMATIONMUSTBEVAILABLETOTHECONTROLALGORITHMATEVERYCONTROLLERSAMTIMEADATAACQUISITIONSOFTWARELABVIEWISUSEDTOVIDETHISINFORMATIONSTRUCTUREOFAFUZZYCONTROLLERINFUZZYPROCESSCONTROL,EXPERTISEISENCAPSULATEDINTOSYSTEMINTERMSOFLINGUISTICDESCRIPTIONSOFKNOWLEDGEHUMANOPERATINGCRITERIA,ANDKNOWLEDGEABOUTTHEOUTPUTRELATIONSHIPSTHEALGORITHMISBASEDONTHESKNOWLEDGE,BUTITALSOINCLUDESCONTROLTHEORY,THEERRORDERIVATIVE,TAKINGINTOCONSIDERATIONTHEDYOFTHEPROCESSTHUS,THECONTROLLERHASASITSINPUTS,CUTTINGFORCEERRORDELTA1FANDITSFIRSTDIFFERENCEDELTA12F,ANDOUTPUTS,THEVARIATIONINFEEDRATEDELTA1FTHEFUZZYCONTROLARIABLESFUZZIFICATIONSEEFIG2ASWELLASTHECREATIONTHERULESBASEWERETAKENFROMTHEEXPERTOPERATORTHEFORCEERRORANDFIRSTDIFFERENCEOFTHEERRORARECALCUATEACHSAMPLINGINSTANTK,ASDELTA1FKFREF−FKAND2FKDELTA1FK−DELTA1FK−1,WHEREFISMEASUREDCUTTINGANDFREFISFORCESETPOINTANDMODELFEEDRATE3ETALANDFORCESSCRIBEDMACHINEFEEDINGFITQUENCEFROMFORMCOMMANDEDTINGMODELMENTALFEEDRATEUZUPERLETAL/JOURNALOFMATERIALSPROCESSINGFIG2STRUCTUREOFAFUZZYCNCMACHININGPROCESSMODELACNCMACHININGPROCESSMODELSIMULATORISUSEDTOVALUATETHECONTROLLERDESIGNBEFORECONDUCTINGEXPERIMENTESTSTHEPROCESSMODELCONSISTSOFANEURALFORCEMODELFEEDDRIVEMODELTHENEURALMODELESTIMATESCUTTINGBASEDONCUTTINGCONDITIONSANDCUTGEOMETRYASDEBYZUPERL1THEFEEDDRIVEMODELSIMULATESTHERESPONSETOCHANGESINCOMMANDEDFEEDRATETHEDRIVEMODELWASDETERMINEDEXPERIMENTALLYBYEXAMINSTEPCHANGESINTHECOMMANDEDVELOCITYTHEBESTMODELWASFOUNDTOBEASECONDORDERSYSTEMWITHANATURALFREYOF3HZANDASETTLINGTIMEOF04SCOMPARISONOFXPERIMENTALANDSIMULATIONRESULTSOFAVELOCITYSTEPCHANGE7TO22MM/SISSHOWNONFIG3THEFEEDDRIVEANDNEURALFORCEMODELARECOMBINEDTOTHECNCMACHININGPROCESSMODELMODELINPUTISTHEFEEDRATEANDTHEOUTPUTISTHEX,YRESULTANTCUTFORCETHECUTGEOMETRYISDEFINEDINTHENEURALFORCETHESIMULATORISVERIFIEDBYCOMPARISONOFEXPERIANDMODELSIMULATIONRESULTSAVARIETYOFCUTSWITHCHANGESWEREMADEFORVALIDATIONCHANGEFIGRESULTSTECHNOLOGYXXX2005XXX–XXX3CONTROLLERTHEEXPERIMENTALANDSIMULATIONRESULTANTFORCEFORASTEPINFEEDRATEFROM005TO2MM/TOOTHISPRESENTEDIN4THEEXPERIMENTALRESULTSCORRELATEWELLWITHMODELINTERMSOFAVERAGEANDPEAKFORCETHEEXPERIMENTALFIG3COMPARISONOFACTUALANDMODELFEDERATE4RESULTSANDTHE31DARDLARIMENTSFORCEUSEDFEDERATEDIALFORCESARYUZUPERLETAL/JOURNALOFMATERIALSPROCESSINGFIG4STRUCTUREOFAFUZZYCORRELATEWELLWITHMODELRESULTSINTERMSOFAVERAGEPEAKFORCETHEOBVIOUSDISCREPANCYMAYBEDUETOINACCURACIESINNEURALMODEL,ANDUNMODELEDSYSTEMDYNAMICSCUTTINGFORCEMODELINGTOREALISETHEONLINEMODELLINGOFCUTTINGFORCES,ASTANBPNEURALNETWORKNNISPROPOSEDBASEDONTHEPOPUBACKPROPAGATIONLEERINGRULEDURINGPRELIMINARYEXPERITPROVEDTOBESUFFICIENTLYCAPABLEOFEXTRACTINGTHEMODELDIRECTLYFROMEXPERIMENTALMACHININGDATAITISTOSIMULATETHECUTTINGPROCESSTHENNFORMODELLINGNEEDSFOURINPUTNEURONSFORMILLINGF,CUTTINGSPEEDVCAXIALDEPTHOFCUTADANDRADEPTHOFCUTRDTHEOUTPUTFROMTHENNARECUTTINGCOMPONENTS,THEREFORETWOOUTPUTNEURONSARENECESTHEDETAILEDTOPOLOGYOFTHEUSEDNNWITHOPTIMALTRAININGALSO732MODELINGFERENTBEREFNETWYSEDDIFANDTHEINPUTCONCLUSIONSTECHNOLOGYXXX2005XXX–XXXCONTROLLERPARAMETERSANDMATHEMATICALPRINCIPLEOFTHENEURONISSHOWNINFIG5BESTNNCONFIGURATIONCONTAINS5,3ANDHIDDENNEURONSINHIDDENLAYERSTOPOLOGYOFNEURALNETWORKANDITSADAPTATIONTOPROBLEMTHEEFFECTOFTOPOLOGYISALSOSTUDIEDBYCONSIDERINGDIFCASESTHETOPOLOGIESAREVARIEDBYVARYINGTHENUMOFNEURONSINHIDDENLAYERSTOEVALUATETHEINDIVIDUALFECTSOFTRAININGPARAMETERSONTHEPERFORMANCEOFNEURALORK40DIFFERENTNETWORKSWERETRAINED,TESTEDANDANALTHENETWORKPERFORMANCESWEREEVALUATEDUSINGFOURFERENTCRITERIA5ETSTMAX,ETST,ETRN,ANDETRNMAXTHENUMBEROFTRAININGCYCLESTHENUMBEROFNEURONSININPUTANDOUTPUTLAYERSAREDETERMINEDBYTHENUMBEROFANDOUTPUTPARAMETERSFROMTHERESULTSTHEFOLLOWINGCANBEDRAWN

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