濰城區(qū)公開選拔年輕干部到基層掛職

DOE-basedAutomaticProcessControlwithConsiderationofModelUncertaintiesJanShiandJing

ZhongTheUniversityofMichigan

C.F.JeffWuGeorgiaInstituteofTechnology1OutlineIntroductionDOE-basedAutomaticProcessControlwithConsiderationofModelUncertaintyProcessmodelControlobjectivefunctionControllerdesignstrategiesSimulationandcasestudySummary2ProblemStatementProcessvariationismainlycausedbythechangeofunavoidablenoisefactors.Processvariationreductioniscriticalforprocessqualityimprovement.OfflineRobustParameterDesign(RPD)usedatthedesignstageTosetanoptimalconstantlevelforcontrollablefactorsthatcanensurenoisefactorshaveaminimalinfluenceonprocessresponsesBasedonthenoisedistributionbutnotrequiringonlineobservationsofnoisefactorsOnlineAutomaticProcessControl(APC)duringproductionWiththeincreasingusageofin-processsensingofnoisefactors,itwillprovideanopportunitytoonlineadjustcontrolfactorstocompensatethechangeofnoisefactors,whichisexpectedtoachieveabetterperformancethanofflineRPD.3MotivationofUsingAPCx=x1enoisedistributiony(x,e)abOnlineadjustXbasedonex=x2Offlinefixx=x2Offlinefixx=x14TheObjectiveandFocus

DOE-BasedAPC

DesignofExperiments(DOE)AutomaticProcessControl(APC)StatisticalProcessControl(SPC)Theresearchfocusesonthedevelopmentofautomaticprocesscontrol(APC)methodologiesbasedonDOEregressionmodelsandreal-timemeasurementorestimationofnoisefactorsforcomplexmfgprocesses5LiteratureReviewForcomplexdiscretemanufacturingprocesses,therelationshipbetweentheresponses(outputs)andprocessvariables(inputs)areobtainedbyDOEusingaresponsesurfacemodel,ratherthanusingdynamicdifferential/differenceequationsofflinerobustparameterdesign(RPD)(Taguchi,1986)Improverobustparameterdesignbasedontheexactleveloftheobserveduncontrollablenoisefactors(Pledger,1996)ExistingAPCliteraturearemainlyforautomaticcontrolofdynamicsystemsthataredescribedbydynamicdifferential/differenceequations.CertaintyEquivalenceControl(CEC)(Stengel,1986):Thecontrollerdesignandstateestimatordesignareconductedseparately(Theuncertaintyofsystemstatesisnotconsideredinthecontrollerdesign)CautiousControl(CC)(AstromandWittenmark,1995):Thecontrollerisdesignedbyconsideringthesystemstateestimationuncertainty,whichisextremelydifficultforacomplexnonlineardynamicsystem.

JinandDing(2005)proposedDoe-BasedAPCconcepts:consideringon-linecontrolwithestimationofsomenoisefactors.Nointeractiontermsbetweennoiseandcontrolfactorsintheirmodel.6ObjectiveDevelopageneralmethodologyforcontrollerdesignbasedonaregressionmodelwithinteractionterms.InvestigateanewcontrollawconsideringmodelparameterestimationuncertaintiesComparetheperformancesofCC,CEC,andRPD,aswellasperformancewithsensinguncertainties.7MethodologyDevelopmentProcedures

APCUsingRegressionResponseModels

BasedonkeyprocessvariableS1:ConductDOEandprocessmodelingObtainsignificantfactors&estimatedprocessmodel

S2:DetermineAPCcontrolstrategy(consideringmodelerrors

S3:Onlineadjustcontrollablefactors

S4:ControlperformanceevaluationBasedonobservationuncertainty

Basedonprocessoperationconstraintsoncontroller

Usecertaintyequivalencecontrolorcautiouscontrol

Obtainreducedprocessvariation81.ProcessVariableCharacterizationProcessVariablesControllableFactorsNoiseFactorsUnobservableNoiseFactorsObservableNoiseFactorsOff-linesettingFactorsOn-lineadjustableFactorsY=f(X,U,e,n)92.ControlSystemFrameworkControllableFactors(x)ManufacturingProcessUnobservableNoiseFactors(n)ObservableNoiseFactors(e)In-ProcessSensingofeResponse(y)ObserverforNoiseFactors(e)Feedforward

ControllerNoiseFactorsPredictedResponseTarget10Observationsofmeasurablenoisefactors,denotedby,areunbiased,i.e.,and .3ControllerDesign

3.1ProblemAssumptionsThemanufacturingprocessisstaticwithsmoothlychangingvariablesovertime–ParameterStabilityEstimatedprocessparametersdenoted

by,isestimatedfromexperimentaldata.e,n

andεareindependent,withE(e)=0,Cov(e)=Σe,E(n)=0,Cov(n)=Σn,E(ε)=0,Cov(ε)=Σε.εarei.i.d.113ControllerDesign

3.2ObjectiveFunctionObjectiveFunction(QuadraticLoss)OptimizationProblem12Step1Off-lineControllableFactorsSettingStep2On-lineAutomaticControlLawProcedureforSolvingOptimizationProblemStep2obtainX*bysolvingoptimizationproblemofJAPC

3ControllerDesign

3.3ControlStrategyStep1ClosedformsolutionofU*bysolvingProcessControlStrategy–TwoStepProcedure134.CaseStudy:

AnInjectionMoldingProcessProcessDescriptionResponseVariable(y):

PercentageShrinkageofMoldedPartsProcessVariables:14DOEModelingReducedDOEModelafterCoefficientSignificanceTestsDesignedExperimentResult(Engel,1992)ParameterEstimationError15RPDSettings

RobustParameterDesignVarianceModelResponseModel,andu1andx3areadjustedaccordingtotargetvaluesasinrighttable16ObjectiveLossFunctionOptimalSettingsDOE-BasedAPCwhere17~~AssumingOptimalOff-lineSettingSimulationResultsComparisonofRPD,CEcontrolandCautiousControlControlStrategyEvaluationCautiouscontrollawperformsmuchbetterthanRPD~18SimulationResults-2CEcontrollerperformsmuchbetterthanRDwhenthemeasurementisperfect,butitsadvantagedecreaseswhenthemeasurementisnotperfect,andwillcausealargerqualitylossthanRPDcontrollerunderhighmeasurementuncertainty.CertaintyEquivalence–assumeobservationperfect19Controlstrategywithpartialsensingfailure–1Sensornoiselevelchange–nomodelingerror150observations,sensornoiselevelincreasedfrompoint51to100,thenrestored.t=1.6CEControlsuffersgreatlyfromnoiselevelchangeMeanofRPDhasdeviatedfromtarget20Controlstrategywithpartialsensingfailure–2255observations,sensornoiselevelincreasedfrompoint101to200,thenrestoredSensornoiselevelchangeOverallJ/J_ce=16.8%.APCperformanceissteadyoverdifferentnoiselevels.–APCconsideringmodelingerror21Controlstrategywithpartialsensingfailure–3Sensorfailure

-Assumenomodelingerror,-250observations,sensorfailedfrompoint51to150,thenrepairedControlStrategySwitchtoRPDsettingafterthedetectionofsensorfailure-Actualsystemwillhavestepresponse22[2]In-processsensingvariables:tonnagesignal,shutheight,vibration,punchspeed,temperature[3]In-processpartsensing:surfaceanddimensionmeasurements[1]Controllablevariables:shutheight,punchspeed,temperature,bindingforcecasterin-processpartformingFormedpartDOE-BasedAPCEstimablenoisefactors:materialproperties(hardness,thickness),gibconditions,die/toolwearInestimablenoisefactors:distributionoflubrication,materialcoatingproperties,dieset-upvariationProcesschangedetectionandon-lineestimationofestimablenoisefactorsIndustrialCollaborationwithOGTechnologies: