Measurement-System-Analy教學(xué)講解課件

MeasurementSystemAnalysisMeasurementSystemAnalysis1ModuleContentMeasurementSystemAnalysisforAttributesComponentsofGageVariationEffectofMeasurementVariabilityAssessingMeasurementVariability2ModuleContentMeasurementSyst2MeasurementSystemAnalysisforAttributesIntherealworld,noteverycharacteristicismeasurable.Insuchcases,aunitisjudgedtobegoodorbadbasedonwhetheritisdefectfreee.g.visualinspectionwhetheritservesitsfunctionalpurposee.g.functionaltesting3MeasurementSystemAnalysisfo3WarmUpExerciseTask:Youhave60secondstodocumentthenumberoftimesthe6thletterofthealphabetappearsinthefollowingtext.TheNecessityofTrainingFarmHandsforFirstClassFarmsintheFatherlyHandlingofFarmLiveStockisForemostintheEyesofFarmOwners.SincetheForefathersoftheFarmOwnersTrainedtheFarmHandsforFirstClassFarmsintheFatherlyHandlingofFarmLiveStock,theFarmOwnersFeeltheyshouldcarryonwiththeFamilyTraditionofTrainingFarmHandsofFirstClassFarmersintheFatherlyHandlingofFarmLiveStockBecausetheyBelieveitistheBasisofGoodFundamentalFarmManagement.4WarmUpExerciseTask:Youhave4AttributeTerminologyAttributeData:Qualitative(go/nogo)datathatcanbetalliedforrecordingandanalysis.AttributeMeasurementSystem:Ameasurementsystemthatcompareseachparttoastandardandacceptsthepartifthisstandardismet.Screen:100%evaluationofproductusinginspectiontechniques(anattributemeasurementsystem).ScreenEffectiveness:Theabilityoftheattributemeasurementsystemtoproperlydiscriminategoodfrombad.CustomerBias:Operatorhasatendencytoholdbackgoodproduct.ProducerBias:Operatorhasatendencytopassdefectiveproduct.5AttributeTerminologyAttribute5AttributeR&R-Method1)Selectaminimumof30partsfromtheprocess.50%ofthepartsinyourstudyshouldhavedefects50%ofthepartsshouldbedefectfreeIfpossibleselectborderline(ormarginal)goodandbadsamples2)Identifytheinspectors,whoshouldbequalifiedandexperienced.3)Haveeachinspector,independentlyandinrandomorder,assessthesepartsanddeterminewhetherornottheypassorfail.4)EnterthedataintotheAttributeR&R.xlsspreadsheettoreporttheeffectivenessoftheattributemeasurementsystem.5)Documenttheresults.Implementappropriateactionstofixtheinspectionprocessifnecessary.6)Re-runthestudytoverifythefix.Note:A30piecesamplewillyieldanestimateofinspectorefficiencyandcapabilitywhichhasafairamountofuncertainty.Typicallyalargersampleisnotneededbecausetheinspectionprocessisobviouslyineffective.Thespreadsheetcanhandleupto100samples.6AttributeR&R-Method1)Sele6AttributeR&R-Method7AttributeR&R-Method77AttributeR&R-ScoringForeachsample–operatorcombinationAppraiserScore=1 ifTrial1=Trial2 AppraiserScore=0 ifotherwiseTheappraiserscorereflectstheconsistencyofanoperatorinassessingthesampleunit.AttributeScore=1 ifTrial1=Trial2=Attribute AttributeScore=0 ifotherwiseTheattributescorereflectstheconsistencyofanoperator’sassessmentofthesampleunitagainstthetrueattributeofthesampleunit.8AttributeR&R-ScoringForeac8AttributeR&R-Scoring9AttributeR&R-Scoring99AttributeR&R-ScoringOverallScreeningEffectivenessOverallAppraiserScore=1ifallappraiserscores=1 OverallAppraiserScore=0ifotherwiseOverallAttributeScore=1 ifallattributescores=1 OverallAttributeScore=0 ifotherwise10AttributeR&R-ScoringOverall10AttributeR&R-Scoring11AttributeR&R-Scoring1111NotesonAttributeR&R(1)If%AppraiserScoreislessthan100%trainingneedstooccur.Focusonspecificareas.(2)%Scorevs.Attributeisanerroragainstaknownpopulationasdeemedbyexperts.(3)100%isthetargetforScreen%EffectivenessScore.(4)Screen%Effectivevs.Attributeisanerroragainstaknownpopulation.100%isthetarget.(5)Attributelegendallowsexceltouseamacrotocountthenumberofoccurrencesofthelegendtext.12NotesonAttributeR&R(1)If%12VariableGR&RStudyofyourmeasurementsystemwillrevealtherelativeamountofvariationinyourdatathatresultsfrommeasurementsystemerror.Itisalsoagreattoolforcomparingtwoormoremeasurementdevicesortwoormoreoperators.MSAshouldbeusedaspartofthecriteriaforacceptinganewpieceofmeasurementequipmenttomanufacturing.Itshouldbethebasisforevaluatingameasurementsystemwhichissuspectofbeingdeficient.Itshouldbepartoftheperiodicmaintenanceprogram.13VariableGR&RStudyofyourmea13TypesofVariations14TypesofVariations1414GageVariation-BiasBiasisthedifferencebetweentheaverageofasetofmeasuredvaluesandthetruevalueofthecharacteristicbeingmeasured.x–BiasTrueValueObservedValue15GageVariation-Biasx–BiasTr15ExampleAnengineerchosefive“goldenunits”thatrepresentedtheexpectedrangeofmeasurements.Twelverandommeasurementsweremadeoneachpart.Meanvalueofthefivestandardis6mmandhistoricalprocessvariationwasfoundtobe12mm.DatacanbefoundintheMeasurementSystemAnalysis.MTWfile.Meanofthesixtymeasurements x =5.9467mmMeanvalueofthefivestandards =6mm Bias =x–=5.9467–6 =–0.0533mm =0.444%ofprocessvariation16ExampleAnengineerchosefive16GageVariation-LinearityLinearityisthedifferenceinbiasthroughouttheexpectedrangeofmeasurements.1217GageVariation-LinearityLine17GageVariation-LinearityLinearitymaybeobtainedviathefollowingprocedure:a) Foreachcase,computetheError(ei),i.e.measuredvalue–truevalueb) Foreachpart,computetheMeanError(ei),i.e.ieinc) DeterminetheslopeforbestfitlineofMeanErrorvsTrueValue

d) Linearity=|Slope|×ProcessVariation18GageVariation-LinearityLine18ExampleComputetheaccuracyandlinearityforthedatainpreviousexample,usingMiniTab’sGageLinearityStudy.StatQualityToolsGageLinearityStudy19ExampleComputetheaccuracyan19Example20Example2020GageVariation-StabilityStabilityistheabilityofthemeasurementsystemtoproducethesameaveragemeasurementsonthesameunitatdifferenttimes.x1–x2–x1–21GageVariation-StabilityStab21GageVariation-StabilityMeasurementsysteminstabilityistheresultofvariousfactors:Time(longidleperiods),numberofmeasurementstaken,airpressurechange,warm-up,drift,etc.Ifthecausativefactor(s)is/areknown,thefrequencyofcalibrationcanbeadjustedaccordinglytominimizetheerrorduetoinstability.22GageVariation-StabilityMeas22GageVariation-PrecisionPrecisionreferstotheabilityofameasurementsystemtoproducethesamevalueonrepeatedmeasurementsofthesameunit.Itismeasuredbythestandarddeviation(orvariance).s1s2System1System223GageVariation-PrecisionPrec23AccuracyvsPrecision1)accuratebutnotprecise 2)precisebutnotaccurate3)neitheraccuratenorprecise 4)bothaccurateandprecise24AccuracyvsPrecision1)accura24AccuracyvsPrecision1)accuratebutnotprecise 2)precisebutnotaccurate3)neitheraccuratenorprecise 4)bothaccurateandprecise25AccuracyvsPrecision1)accura25ComponentsofPrecisionPrecisionmaybebrokendownintotwoseparateparts:RepeatabilitytheabilityofameasurementsystemtorepeatameasurementonthesameunitunderthesameoperatingconditionsReproducibilitytheabilityofameasurementsystemtoreproduceameasurementonthesameunitunderdifferentmeasurementconditions26ComponentsofPrecisionPrecisi26SourceofVariabilityProcessVariabilitytruevariabilitywithinandbetweenunitsduetovariationinproductionprocess,rawmaterial,equipmentconditionandenvironmentMeasurementVariabilityvariabilityoverandabovetheactualunit-to-unitvariability,arisingfromthemeasurementitself27SourceofVariability2727BasicModelProductVariability(Actualvariability)MeasurementVariabilityTotalVariability(Observedvariability)+=28BasicModelProductVariability28Whichoneisgood?Line1Line2TotalVariability(observed)MeasurementVariabilityTrueProcessVariabilitys2(trueprocess)+s2

(meas)=s2

(observed)29Whichoneisgood?Line1Line229BasicModelObservedvalue=mastervalue+measurementoffsetObservedvariability=productvariability+measurementvariabilityMeasurementSystemBias:assessedthroughcalibration.(accuracy)MeasurementSystemVariability:assessedthroughthevariableR&Rstudy(precision)TruevaluesMeasuredvaluesmeasurementoffsetTruevaluesMeasuredvalues30BasicModelObservedvalue=30SourcesofMeasurementVariation'Measurement

Variation'HumidityCleanlinessVibrationLine

Voltage

VariationTemperature

FluctuationOperator

TechniqueStandard

ProceduresSufficient

Work

TimeMaintenance

StandardCalibration

FrequencyOperator

TrainingEase

of

Data

EntryAlgorithm

InstabiltyElectrical

InstabilityWearMechanicalinstabilityToolEnvironmentWorkMethods31SourcesofMeasurementVariati31DiscriminationThenumberofdecimalplacesthatcanbemeasuredbythesystem.Incrementsofmeasureshouldbeatleastone-tenthofthewidthoftheproductspecificationorprocessvariation.12Whichrulershouldbeusedtomeasurepartsfortheprocessrepresentedbythedistributionabove?32DiscriminationThenumberofde32AssessingMeasurementVariabilityThreecommonlyusedcriteria:? or? or ? orPrecisiontoToleranceRatioor%Toelrance%R&Ror%contribution?(DistinctCategories)233AssessingMeasurementVariabil33EffectofMeasurementVariability(1)ExcessiveMeasurementVariability AcceptableMeasurementVariabilityProcessVarianceMeasurementVarianceTotalVarianceProcessVarianceMeasurementVarianceTotalVarianceObservedProcessCapabilityActualProcessCapability34EffectofMeasurementVariabil34EffectofMeasurementVariability(1A)From Cp ObservedCp

ActualCp35EffectofMeasurementVariabil35Effectof2Measurement/2Total36Effectof2Measurement/2To36EffectofMeasurementVariability(1B)Alternatively,37EffectofMeasurementVariabil37Effectof5.15

(Measurement/Tolerance)38Effectof5.15(Measurement38EffectofMeasurementVariability(2)UnitisGoodUnitisBadPr(UnitisAccepted)Pr(UnitisRejected)39EffectofMeasurementVariabil39GageRepeatability&ReproducibilityStudy40GageRepeatability&Reproduci40GageRepeatability&ReproducibilityStudyMethod1:XandRBreaksdowntheoverallvariationintothreecategoriespart-to-partrepeatabilityReproducibility(Willnotbediscussed)Method2:ANOVAFurtherbreakdowninthereproducibilitycomponentpart-to-partrepeatabilityreproducibility —operatormaineffect —operator×partinteraction41GageRepeatability&Reproduci41ExampleABlackBeltseekstoassessthecapabilityofoxygenanalyzersusedinthemeasurementofoxygencontentinnitrogen-purgedreflowovens.Fourflowratesofnitrogenwereselectedforhisstudy.Twoanalyzerswererandomlyselected.Verifyifthecurrentoxygenanalyzersareadequate.Thetoleranceoftheprocess300.ThedatacanbefoundinMeasurementSystemAnalysis.MTW.42ExampleABlackBeltseekstoa42ExampleStatQualityToolsGageR&RStudy(Crossed)43ExampleStatQualityTools43ExampleSessionWindowGageR&RStudy-ANOVAMethodGageR&R %ContributionStdDevStudyVar%StudyVar%ToleranceSource

VarComp

(ofVarComp)

(SD)

(5.15*SD)

(%SV)

(SV/Toler)TotalGageR&R5.4507.382.334512.022727.164.01Repeatability0.4000.540.63223.25567.361.09Reproducibility5.0506.842.247311.573626.153.86Analyzer3.7245.041.92989.938222.453.31Analyzer*FlowRate1.3261.801.15175.931213.401.98Part-To-Part68.41092.628.271042.595896.2414.20TotalVariation73.860100.008.594244.2600100.0014.75NumberofDistinctCategories=544ExampleSessionWindow4444NumberofDistinctCategoriesThenumberofdistinctcategoriesrepresentsthenumberofgroupswithintheprocessdatathatthemeasurementsystemcandiscern.

If10differentpartswereusedinthegagemeasurementstudy,and4distinctcategoriesweredistinguished.Thismeansthatsomeofthose10partsarenotdifferentenoughtobediscernedasbeingdifferentbythemeasurementsystem.Highernumberofdistinctcategoriesimpliesamoreprecisegage.45NumberofDistinctCategories445NumberofDistinctCategoriesAutomobileIndustryActionGroup(AIAG)recommendations:Categories Remarks <2 Systemcannotdiscernonepartfromanother =2 Systemcanonlydividedataintwogroups e.g.highandlow =3 Systemcanonlydividedatainthreegroups e.g.low,middleandhigh

4 Systemisacceptable46NumberofDistinctCategoriesA46UsesofP/TandP/PV(%R&R)TheP/Tratioisthemostcommonestimateofmeasurementsystemprecision.Thisestimatemaybeappropriateforevaluatinghowwellthemeasurementsystemcanperformwithrespecttothespec.Specifications,however,mustbeappropriatelyselected.Generally,theP/Tratioisagoodestimatewhenthemeasurementsystemisonlyusedtoclassifyproductionsamples.Eventhen,ifprocesscapability(Cpk)isnotadequate,theP/Tratiomaygiveyouafalsesenseofsecurity.TheP/TV(%R&R)isthebestmeasurefortheBlackBelt.Thisestimateshowwellthemeasurementsystemperformswithrespecttotheoverallprocessvariation.%R&Risthebestestimatewhenperformingprocessimprovementstudies.Caremustbetakentousesamplesrepresentingfullprocessrange.47UsesofP/TandP/PV(%R&R)The47DistinctCategoriesvs%ContributionCategories

%Contribution2 33.3%3 18.2%4 11.1%5 7.4%6 5.3%7 3.9%8 3.0%48DistinctCategoriesvs%Contr48NestedGR&RFortheGRRStudyontheoxygenanalyzers,atanygivenflowrate,theactual“samplei”measuredbythetwoanalyzersareactuallydifferent.Forthatmatter,repeatedmeasuresbyananalyzerforanygivenflowrateareactuallymadeondifferent“samples”.Suchsituationsarecharacteristicofdestructivetesting.Insuchcases,aNestedGRRStudyshouldbeperformed.49NestedGR&RFortheGRRStudyo49ExampleStatQualityToolsGageR&RStudy(Nested)50ExampleStatQualityTools50Example51Example5151ExampleSessionWindowGageR&RStudy-ANOVAMethodGageR&R%ContributionStdDevStudyVar%StudyVar%ToleranceSource

VarComp

(ofVarComp)

(SD)

(5.15*SD)

(%SV)

(SV/Toler)TotalGageR&R0.39960.570.632163.25567.551.09Repeatability0.39960.570.632163.25567.551.09Reproducibility0.00000.000.000000.00000.000.00Part-To-Part69.736399.438.3508243.006799.7114.34TotalVariation70.1359100.008.3747243.1298100.0014.38NumberofDistinctCategories=1952ExampleSessionWindow5252

EndofTrainingRev1;9May02

EndofTrainingRev1;9May053MeasurementSystemAnalysisMeasurementSystemAnalysis54ModuleContentMeasurementSystemAnalysisforAttributesComponentsofGageVariationEffectofMeasurementVariabilityAssessingMeasurementVariability55ModuleContentMeasurementSyst55MeasurementSystemAnalysisforAttributesIntherealworld,noteverycharacteristicismeasurable.Insuchcases,aunitisjudgedtobegoodorbadbasedonwhetheritisdefectfreee.g.visualinspectionwhetheritservesitsfunctionalpurposee.g.functionaltesting56MeasurementSystemAnalysisfo56WarmUpExerciseTask:Youhave60secondstodocumentthenumberoftimesthe6thletterofthealphabetappearsinthefollowingtext.TheNecessityofTrainingFarmHandsforFirstClassFarmsintheFatherlyHandlingofFarmLiveStockisForemostintheEyesofFarmOwners.SincetheForefathersoftheFarmOwnersTrainedtheFarmHandsforFirstClassFarmsintheFatherlyHandlingofFarmLiveStock,theFarmOwnersFeeltheyshouldcarryonwiththeFamilyTraditionofTrainingFarmHandsofFirstClassFarmersintheFatherlyHandlingofFarmLiveStockBecausetheyBelieveitistheBasisofGoodFundamentalFarmManagement.57WarmUpExerciseTask:Youhave57AttributeTerminologyAttributeData:Qualitative(go/nogo)datathatcanbetalliedforrecordingandanalysis.AttributeMeasurementSystem:Ameasurementsystemthatcompareseachparttoastandardandacceptsthepartifthisstandardismet.Screen:100%evaluationofproductusinginspectiontechniques(anattributemeasurementsystem).ScreenEffectiveness:Theabilityoftheattributemeasurementsystemtoproperlydiscriminategoodfrombad.CustomerBias:Operatorhasatendencytoholdbackgoodproduct.ProducerBias:Operatorhasatendencytopassdefectiveproduct.58AttributeTerminologyAttribute58AttributeR&R-Method1)Selectaminimumof30partsfromtheprocess.50%ofthepartsinyourstudyshouldhavedefects50%ofthepartsshouldbedefectfreeIfpossibleselectborderline(ormarginal)goodandbadsamples2)Identifytheinspectors,whoshouldbequalifiedandexperienced.3)Haveeachinspector,independentlyandinrandomorder,assessthesepartsanddeterminewhetherornottheypassorfail.4)EnterthedataintotheAttributeR&R.xlsspreadsheettoreporttheeffectivenessoftheattributemeasurementsystem.5)Documenttheresults.Implementappropriateactionstofixtheinspectionprocessifnecessary.6)Re-runthestudytoverifythefix.Note:A30piecesamplewillyieldanestimateofinspectorefficiencyandcapabilitywhichhasafairamountofuncertainty.Typicallyalargersampleisnotneededbecausetheinspectionprocessisobviouslyineffective.Thespreadsheetcanhandleupto100samples.59AttributeR&R-Method1)Sele59AttributeR&R-Method60AttributeR&R-Method760AttributeR&R-ScoringForeachsample–operatorcombinationAppraiserScore=1 ifTrial1=Trial2 AppraiserScore=0 ifotherwiseTheappraiserscorereflectstheconsistencyofanoperatorinassessingthesampleunit.AttributeScore=1 ifTrial1=Trial2=Attribute AttributeScore=0 ifotherwiseTheattributescorereflectstheconsistencyofanoperator’sassessmentofthesampleunitagainstthetrueattributeofthesampleunit.61AttributeR&R-ScoringForeac61AttributeR&R-Scoring62AttributeR&R-Scoring962AttributeR&R-ScoringOverallScreeningEffectivenessOverallAppraiserScore=1ifallappraiserscores=1 OverallAppraiserScore=0ifotherwiseOverallAttributeScore=1 ifallattributescores=1 OverallAttributeScore=0 ifotherwise63AttributeR&R-ScoringOverall63AttributeR&R-Scoring64AttributeR&R-Scoring1164NotesonAttributeR&R(1)If%AppraiserScoreislessthan100%trainingneedstooccur.Focusonspecificareas.(2)%Scorevs.Attributeisanerroragainstaknownpopulationasdeemedbyexperts.(3)100%isthetargetforScreen%EffectivenessScore.(4)Screen%Effectivevs.Attributeisanerroragainstaknownpopulation.100%isthetarget.(5)Attributelegendallowsexceltouseamacrotocountthenumberofoccurrencesofthelegendtext.65NotesonAttributeR&R(1)If%65VariableGR&RStudyofyourmeasurementsystemwillrevealtherelativeamountofvariationinyourdatathatresultsfrommeasurementsystemerror.Itisalsoagreattoolforcomparingtwoormoremeasurementdevicesortwoormoreoperators.MSAshouldbeusedaspartofthecriteriaforacceptinganewpieceofmeasurementequipmenttomanufacturing.Itshouldbethebasisforevaluatingameasurementsystemwhichissuspectofbeingdeficient.Itshouldbepartoftheperiodicmaintenanceprogram.66VariableGR&RStudyofyourmea66TypesofVariations67TypesofVariations1467GageVariation-BiasBiasisthedifferencebetweentheaverageofasetofmeasuredvaluesandthetruevalueofthecharacteristicbeingmeasured.x–BiasTrueValueObservedValue68GageVariation-Biasx–BiasTr68ExampleAnengineerchosefive“goldenunits”thatrepresentedtheexpectedrangeofmeasurements.Twelverandommeasurementsweremadeoneachpart.Meanvalueofthefivestandardis6mmandhistoricalprocessvariationwasfoundtobe12mm.DatacanbefoundintheMeasurementSystemAnalysis.MTWfile.Meanofthesixtymeasurements x =5.9467mmMeanvalueofthefivestandards =6mm Bias =x–=5.9467–6 =–0.0533mm =0.444%ofprocessvariation69ExampleAnengineerchosefive69GageVariation-LinearityLinearityisthedifferenceinbiasthroughouttheexpectedrangeofmeasurements.1270GageVariation-LinearityLine70GageVariation-LinearityLinearitymaybeobtainedviathefollowingprocedure:a) Foreachcase,computetheError(ei),i.e.measuredvalue–truevalueb) Foreachpart,computetheMeanError(ei),i.e.ieinc) DeterminetheslopeforbestfitlineofMeanErrorvsTrueValue

d) Linearity=|Slope|×ProcessVariation71GageVariation-LinearityLine71ExampleComputetheaccuracyandlinearityforthedatainpreviousexample,usingMiniTab’sGageLinearityStudy.StatQualityToolsGageLinearityStudy72ExampleComputetheaccuracyan72Example73Example2073GageVariation-StabilityStabilityistheabilityofthemeasurementsystemtoproducethesameaveragemeasurementsonthesameunitatdifferenttimes.x1–x2–x1–74GageVariation-StabilityStab74GageVariation-StabilityMeasurementsysteminstabilityistheresultofvariousfactors:Time(longidleperiods),numberofmeasurementstaken,airpressurechange,warm-up,drift,etc.Ifthecausativefactor(s)is/areknown,thefrequencyofcalibrationcanbeadjustedaccordinglytominimizetheerrorduetoinstability.75GageVariation-StabilityMeas75GageVariation-PrecisionPrecisionreferstotheabilityofameasurementsystemtoproducethesamevalueonrepeatedmeasurementsofthesameunit.Itismeasuredbythestandarddeviation(orvariance).s1s2System1System276GageVariation-PrecisionPrec76AccuracyvsPrecision1)accuratebutnotprecise 2)precisebutnotaccurate3)neitheraccuratenorprecise 4)bothaccurateandprecise77AccuracyvsPrecision1)accura77AccuracyvsPrecision1)accuratebutnotprecise 2)precisebutnotaccurate3)neitheraccuratenorprecise 4)bothaccurateandprecise78AccuracyvsPrecision1)accura78ComponentsofPrecisionPrecisionmaybebrokendownintotwoseparateparts:RepeatabilitytheabilityofameasurementsystemtorepeatameasurementonthesameunitunderthesameoperatingconditionsReproducibilitytheabilityofameasurementsystemtoreproduceameasurementonthesameunitunderdifferentmeasurementconditions79ComponentsofPrecisionPrecisi79SourceofVariabilityProcessVariabilitytruevariabilitywithinandbetweenunitsduetovariationinproductionprocess,rawmaterial,equipmentconditionandenvironmentMeasurementVariabilityvariabilityoverandabovetheactualunit-to-unitvariability,arisingfromthemeasurementitself80SourceofVariability2780BasicModelProductVariability(Actualvariability)MeasurementVariabilityTotalVariability(Observedvariability)+=81BasicModelProductVariability81Whichoneisgood?Line1Line2TotalVariability(observed)MeasurementVariabilityTrueProcessVariabilitys2(trueprocess)+s2

(meas)=s2

(observed)82Whichoneisgood?Line1Line282BasicModelObservedvalue=mastervalue+measurementoffsetObservedvariability=productvariability+measurementvariabilityMeasurementSystemBias:assessedthroughcalibration.(accuracy)MeasurementSystemVariability:assessedthroughthevariableR&Rstudy(precision)TruevaluesMeasuredvaluesmeasurementoffsetTruevaluesMeasuredvalues83BasicModelObservedvalue=83SourcesofMeasurementVariation'Measurement

Variation'HumidityCleanlinessVibrationLine

Voltage

VariationTemperature

FluctuationOperator

TechniqueStandard

ProceduresSufficient

Work

TimeMaintenance

StandardCalibration

FrequencyOperator

TrainingEase

of

Data

EntryAlgorithm

InstabiltyElectrical

InstabilityWearMechanicalinstabilityToolEnvironmentWorkMethods84SourcesofMeasurementVariati84DiscriminationThenumberofdecimalplacesthatcanbemeasuredbythesystem.Incrementsofmeasureshouldbeatleastone-tenthofthewidthoftheproductspecificationorprocessvariation.12Whichrulershouldbeusedtomeasurepartsfortheprocessrepresentedbythedistributionabove?85DiscriminationThenumberofde85AssessingMeasurementVariabilityThreecommonlyusedcriteria:? or? or ? orPrecisiontoToleranceRatioor%Toelrance%R&Ror%contribution?(DistinctCategories)286AssessingMeasurementVariabil86EffectofMeasurementVariability(1)ExcessiveMeasurementVariability AcceptableMeasurementVariabilityProcessVarianceMeasurementVarianceTotalV