SPC英文版教材PPT课件

.,Module3:StatisticalProcessControl(SPC)Methodology,.,2,PCSElements,CreateMeasurementPlan,EstablishMonitor(SPC),ImplementResponseFlowChecklist(RFC),Element1,Element2,Element3,.,3,Contents,Introduction簡介WhatisSPC什麼是SPC?WhatisStability什麼是穩定性?WhatisaControlChart什麼是管制圖HowtoSet-upaControlChart如何建立管制圖TypeofControlChartsAvailable管制圖的種類HowtoCalculatetheControlLimits如何計算管制界限SPCTrendRulesSPC法則WhentoReviseControlLimits何時重新計算管制界限ProcessCapabilityStudy制程能力研討SpeclimitsVSControlLimits規格界限vs.管制界限StabilityVSCapability穩定性vs.能力ControlChartReduction/Elimination減少管制圖SPCExpectations,.,4,WhatisSPC?,Statistical統計Anythingthatdealswiththecollection,analysis,interpretation&presentationofnumericaldata關於數據資料的收集,分析,解釋與表現Gaininginformationformakinginformeddecisions取得資訊來作有效的決定Process制程Combinationofmachines,tools,methods,materials&peopleemployedtoattainprocessspecification結合機器,治工具,方法,材料與人員來達到制程規格Asimilarprocedure/eventthatishappeningrepetitively重覆發生的事件/類似程序Control管制Tokeepsomethingwithinadesiredcondition使某事/物保持在想要的情況Makesomethingbehavethewaywewantittobehave使某事/物依我們所想的來執行,Theuseofstatisticaltechniquessuchascontrolchartstoanalyzeaprocess,takeappropriateactionstoachieve&maintainastableprocess,&improveprocesscapability.,.,5,WhatisStability?,AprocessissaidtobeStableifithasthefollowingproperties:下列特性稱為穩定:Patternappearsrandom隨機出現Constantprocessmean平均值一定Uniformvariabilityovertime變異程度不隨時間改變Notrends,runs,shifts,erraticups&downs不會偏向一邊Importantformanyreasons:穩定性為何重要?Increasedproductivityofengineering&manufacturingpersonnel提高生產性Predictable,repeatableresultswithinaspecifiedrange結果有重覆性,可預測,.,6,WhatisaControlChart?,Atrendchartwithcontrollimits有管制界限的趨勢圖Graphicalrepresentationofprocessperformance,wheredataiscollectedatregulartimesequenceofproduction數據依生產順序定時間收集,以圖表表現制程性能Valuabletoolfordifferentiatingbetweencommoncauseandspecialcausevariation將一般變異與特殊變異區分開的有用工具Evaluatingwhetheraprocessisorisnotinastateofstatisticalcontrol評估制程是否在統計管制中Itletsthedatatalkbyitself&basisfordata-drivendecisions讓數據說話並依數據導向作決定,.,7,ControlLimits,Atypicalcontrolchartconsistsofthreelines:典型管制圖有三條線:,CL:Theaverage(measureoflocation)processperformancewhentheprocessisin-controlCL:制程的平均性能UCL&LCL:Therangeofusualprocessperformancewhentheprocessisstable.Linesdrawn3standarddeviations(3sigma)oneachsideofthecenterline.UCL&LCL:制程穩定情況下,制程性能的範圍,.,8,ControlChartAssumptions,ProcessStability制程穩定TheprocessmustbeinstatisticalcontrolNormality常態分布TheunderlyingprocessdistributionisnormalNote:Iftheassumptionsarenotmet,thecontrollimitscalculatedaremisleading&donotaccuratelyindicate3sigmacontrollimits.Seeyoursitestatisticianforadviceoncalculationmethodswhenassumptionsareviolated.若假設不成立,則管制界限將沒有意義,.,9,TestforControlChartAssumptions假設,ProcessStability(nooutliers)穩定性Screenoutoutliersfromthedatabasebeforecomputingfinalcontrollimitsbyusingacontrolchart.Anypointbeyondeithercontrollimitisanoutlier.Reportnumberofoutliersscreened.-計算管制界限前,將超出點排除.所有超出管制界限的點都是outlierNormality常態性Plotanormalprobabilityplotofthedataoroverlayanormalcurveoverthehistogram.Normallydistributeddatawillroughlyfallonastraightline.TestfornormalitybyusingShapiro-WilkWtestinJMP用JMPWtest來計算常態性,.,10,Selectappropriatetypeofcontrolcharttobeused選擇合適的管制圖型態Gatherdatatoestablishthecontrolchart.收集數據建立管制圖Aminimumof30subgroupsisrequiredoveratimeframeasdeterminedbythesamplingplan.抽樣計劃至少收集30組數據PlotthedataintimeorderonaTrendChart依序在趨勢圖上描點,HowtoSet-upaControlChart?(I),.,11,Computethecontrollimits&plotthemonthetrendchart計算管制界線並畫在圖上Outliersidentification&exclusion超出點的確認與排除ExcludetheOut-ofControl(OOC)pointsoroutliersforwhichthereareverified/confirmedspecialcausesfromthechart由於顯示是特殊原因造成故將排除超出點Re-computethecontrollimits,excludingtheOOCpoints重新計算管制界限Iftherearefewerthan30pointsremainingatanytime,collectmoredata.Itsveryimportantthatthecontrollimitsarecalculatedusingatleast30subgroups.若資料點少於30再繼續收集.這是很重要的,HowtoSet-upaControlChart?(II),Note:RefertoAppendixAforControlChartsforLimitedProduction,i.e.=10n=10,X,-whensubgroupingisnotapplicable,(Individual),duetosingleunitreadingmaytake,Chart,alongtime,unitreadingisextremely,expensive,etc.,-whenitscommontohavesingle,measurementspacedtimeapart,ControlChartsForVariables計量型,每次量測費時或昂貴,使同一段時間內量測多次不適當,每次量測值都非常相近,同一段時間內量測多次,.,15,WhyMRMethodisusedtodetermineControlLimitsforMean&Variability(Range&StandardDeviation)Chart?為什麼要使用移動全距方法?,Mostbatchproductionprocesseshavealargerrun-to-runvariationthanwithin-runvariation批量性生產時,子群組間的變異大多會大於子群組內的變異Traditionalcontrolchartformulasdevelopedinthe20sbyWalterShewhartconsiderablyunderestimatecontrollimits,i.e.toonarrow傳統的方法將使管制界限太窄,.,16,Traditionalvs.MRMethod,Traditionalcontrolchartformulasareused.,MovingRange(MR)Methodisused.,X-barControlChart,X-barControlChart,.,17,X-SChartConcept,ConsistsofTwoPortions:XChartPlotsthemeanoftheXvaluesinthesample以抽樣的平均值描點Showsthechangesofthemeanofonesampletoanother顯示抽樣平均值的改變SChartPlotsthestandarddeviationofasample以抽樣的標準差描點Showsthechangesindispersionorprocessvariabilityofonesampletoanother顯示抽樣標準差的改變,.,18,ComputingControlLimitsforX-SChart,Obtainatleastk=30subgroups獲得至少30組子群組ComputetheMeanforeachsubgroupofsizen計算每子群組(個數n)的平均值ComputetheStandardDeviationforeachsubgroup計算每子群組(個數n)的標準差ComputetheMovingRangeforeachsubgroupmean,MRXi=|Xi-Xi-1|計算每子群組平均值的移動全距ComputetheMovingRangeforeachsubgrouprange,MRSi=|Si-Si-1|計算每子群組標準差的移動全距,.,19,ComputingControllimitsforX-SChart,ComputetheOverallMean,X=(X1+X2+X3.+Xk)/kComputetheAverageofRange,S=(S1+S2+S3.+Sk)/kComputetheAverageofMovingRangeforthemean,MRX=(MRX2+MRX3+MRX4.+MRXk)/(k-1)ComputetheAverageofMovingRangefortherange,MRS=(MRS2+MRS3+MRS4.+MRSk)/(k-1),=,.,20,ComputetheControlLimits:DrawthecontrollimitsonboththeX-SchartrespectivelyIfLCL(S)0,putas0orN/A,XChartUCL(X)=X+2.66MRXCL(X)=XLCL(X)=X-2.66MRX,=,=,=,ComputingControllimitsforX-SChart,SChartUCL(S)=S+2.66MRSCL(S)=SLCL(S)=S-2.66MRS,.,21,Observations,Mean,MovingRange,S.D.,MovingRange,Subgroup#,1,2,3,4,5,(X-bar),(MRX,),(S),(MR,S,),1,8.0,7.7,8.1,8.0,7.8,7.92,-,0.16,-,2,7.1,6.9,7.4,7.3,7.2,7.18,0.74,0.19,0.03,3,8.0,7.5,7.6,7.8,7.9,7.76,0.58,0.21,0.02,:,:,30,7.5,7.8,7.9,7.8,7.6,7.72,0.70,0.16,0.04,Average,7.64,0.68,0.19,0.03,XChartUCL(X)=X+2.66MRX=7.64+2.66(0.68)=9.45CL(X)=X=7.64LCL(X)=X-2.66MRX=7.64-2.66(0.68)=5.83,ExampleofComputingControlLimitsforX-SChart,SChartUCL(S)=S+2.66MRS=0.19+2.66(0.03)=0.27CL(S)=S=0.55LCL(S)=S-2.66MRS=0.19-2.66(0.03)=0.11,.,22,OpenthedatasetThickness.jmp.1.Computethemeanforeachlot.SelectSummaryfromtheTablesmenu.SelectLotastheGroupvariable.HighlightThickness&selectMeanfromtheStatisticsmenu.Then,highlightThickness&selectStdDevfromtheStatisticsmenu.ClickOK.2.Createanindividualscontrolchartusingthetableoflotmeans&ranges.SelectControlChartfromtheGraphmenu.SelectMean(thickness)&StdDev(Thickness)astheProcessvariable.SelectLotastheSampleLabelvariable.Verifyoptionsettings.ChartTypeis“IR”.IndividualMeasurementboxisselected.MovingRangeboxisnotselected.K-sigmaisselected,andK=3.RangeSpan=2.ClickonOK.,ExampleofComputingControlLimitsforX-SChartusingJMP,.,23,WARNINGS:Group/Summarywillsortthenewtableinalphabeticalorderofthegroupingvariable.Controlchartsmustalwaysbeplottedintimeorder.Therefore,ifthesummarytableisnotintimeorder,youwillhavetosortthetableincorrecttimeorderbeforemakingthecontrolchart.,ExampleofComputingControlLimitsforX-SChartusingJMP,LCL(S)=0,.,24,Exercise1,OpenthedatasetExer1.jmp.ComputetheX-ScontrollimitsusingJMPforleadwidth.-Whatarethecontrollimits?-Istheprocessstable?,.,25,InterpretationofX-SChart,Somespecialcausesofout-of-controlforXChartChangesinmachinesettingoradjustment參數設定被調整MS-to-MStechniqueinconsistentChangesinmaterial材料變化SChartMachineinneedofrepairoradjustment機器須維修NewMsesMaterialsarenotuniform材料一致性不夠,.,26,AttributesControlCharts,Attributecontrolchartsareusefulwhenitisdifficultorimpracticaltomonitoraprocessnumerically(onacontinuousscale)若無法以量測數值來監控制程或有困難時,可使用計數型管制圖Adefectisanindividualfailuretomeetasinglerequirement不良是指無法滿足單一要求Adefectiveunitisaunitthatcontainsoneormoredefects不良品不只包含一項缺點,.,27,ControlChartsForAttributes,.,28,pChartConcept,Itplotsproportionofdefectiveunitsinasample每一抽樣點是以不良率來描點Theproportionofdefectiveunitsinasamplecanbeintermsoffraction,percentordpm不良的比率可以是分數,%,dpm來表示Itallowsustochartproductionprocesseswheresamplesizecannotbeequal不同的抽樣數是允許的,.,29,ComputingControlLimitsforpChartwithMR-Method,Obtainatleastk=30subgroupsorlots.Datacollectedin#ofunitsinspected&#ofunitsrejected.至少30組子群組.以檢驗數與拒收數來收集數據Computethedefectiveratefromtheithlot(i=1,2,.,k),pi=#ofunitsrejected/#ofunitsinspectedComputethecontrollimitsusing:UCL(p)=p+2.66MRpCL(p)=pLCL(p)=p-2.66MRp,WhenLCLUCLorPointLCL至少須使用第1條ForanautomatedSPCsystemwithautomatedapplicationofSPCtrendrules,itshighlyrecommendedtoadd5thruletodetectlargeshiftsinmean,(i.e.2outof3rule)若spc系統是自動的,非常建議增加第5條AddotherrulesdependinguponprocessknowledgeabilitytorespondcriticalityofthemonitorsensitivityrequirementsforthemonitorInteldoesrecommendasagoaltouserules1,2,5,&6whenappropriateIntel建議使用1,2,5,&6條,.,49,TrendRuleRecommendations,Onlyusethetrendrulesthatsignalprocessinstabilitiesforwhichyouarecapableofresponding在你有能力反應處理的不穩定制程,才使用趨勢規則JustificationneededfornotusingotherSPCtrendrulesStddev&rangechartsmaychoosenottoreacttoPoint1Definitelyaproblem:|ChangeRatio|1.5確定有問題1.5,.,GivenThickness.jmpexample:UCLcurrent=130.0LCLcurrent=70.0Newlycollecteddataresultedthefollowing:UCLcalc=119.41LCLcalc=78.72srun-run(calc)=2.66MR/3=20.35/3=6.78UCLChangeRatio=(UCLcalc-UCLcurrent)/srun-run(calc)=-1.56LCLChangeRatio=(LCLcurrent-LCLcalc)/srun-run(calc)=-1.28=Indicatesaneedtochangethecurrentcontrollimits!,ChangeRatioExample,.,56,ProcessCapability,Processcapabilityistheabilityofaprocesstomeetspecifications.Aprocessmustbestablebeforeitscapabilitycanbecomputed.NotCapableCapableAcapabilityindexisastatisticthatquantifies&describesthecapabilityofaprocess,.,57,SpecificationLimits,Theregionwhereproductisknowntofunctionwellintermsofperformance,yield,reliability,orotherdesiredoutcomeAcceptablerangeofvaluesforaproductparameterDefinewhatisacceptable/unacceptableproductDeterminedbyDesignrequirements&simulationmodelsEngineeringjudgement(typicallyproducteng.&integration)Customeragreement/requirementsDatadrivenvalidation:ProcesswindowcharacterizationHistoricaldataidentifyingin-lineorEOLproblemsUsedtodetermineprocesscapability,.,58,ControlLimits,Calculatedfromdata,basedonactualprocessperformanceDescribethenaturalrangeofperformanceofastableprocessDescribetheamountofnaturalprocessvariationUsedtodetermineprocessstability,.,59,SpecLimitsvs.ControlLimits,SpecLimitsBasedonperformancerequiredoftheproductWhatthecustomerwants-“whatwewant”Tellsuswhentodispositiontheproduct/materialApplyonlytoindividual(raw)datavalues,ControlLimitsBasedonactualhistoricalprocessperformanceWhattheprocessdelivers-“whatweget”Tellsuswhentotakeactionontheprocess/equipmentApplytosummarystatistics(e.g.:X-bar,stddev,range,etc.charts),Neverusespeclimitsonacontrolchart!,.,60,Stabilityvs.Capability,Aprocessissaidtobeinstatisticalcontrolwhentheonlysourceofvariationisofnaturalcauses,(i.e.nospecialcausesvariationpresent)AprocessissaidtobecapablewhenvariationfromnaturalcausesisreducedsuchthatitcanmeetproductspecificationtolerancewhenthecontrollimitsarewellwithinthespecificationlimitsAprocessissaidtobenotcapableifthecontrollimitsareoutsidethespecificationlimits,.,61,Exercise3,0,2,4,6,8,Interpretation:Y/N_Stable_Capable,Interpretation:Y/N_Stable_Capable,.,62,Interpretation:Y/N_Stable_Capable,Interpretation:Y/N_Stable_Capable,Exercise3,.,63,MeasuringProcessCapability,Aminimumof30datapointsareneededbeforecalculatingprocesscapabilityindicesAlwaysuseahistogramorwithspecificationlimitstovisuallyrepresenttheprocessdistribution&capability,.,64,MeasuringProcessCapability:Cpk,Cpkisacommonmeasureofprocesscapability:Cpk=minimum(Cpu,Cpl)USL-XX-LSLCpu=Cpl=3sindiv3sindivwhere:USL,LSL-speclimitsforindividualdatavaluessindiv=stddevofindividual(raw)measurements,.,65,Cpkcomparestheupperhalfofthedistributiontotheupperspec&lowerhalfofthedistributiontothelowerspec,MeasuringProcessCapability:Cpk,.,66,ThevalueofCpkisaffectedby:wheretheprocessiscenteredprocessvariationThehighertheCpkvalue,thebetteritisForone-sidedspecification,Cpk=CpuifonlyUSLexistsCpk=CplifonlyLSLexists,MeasuringProcessCapability:Cpk,.,67,AsimplemeasureofprocesscapabilityisCpUSL-LSLCp=-6sindivCpdoesnotconsiderwheretheprocessiscentered,thereforeitassumestheprocessiscenteredontarget.Therefore,CpCpk.Cpisoftencalledprocesspotential:HowtheprocesscouldperformifitwasontargetCpcanonlybecomputedforprocesseswithtwo-sidedspeclimits,MeasuringProcessCapability:Cp,.,68,Example,Cpk=1.44,Cp=1.45Target&meanareclosetogether:Cpk&CparesimilarLSL=700,Target=1000,USL=1300Mean=1003.8,Stddev=68.6,Cpk=0.52,Cp=0.87Target&meanareoffsetsignificantly:CpkismuchlessthanCpLSL=700,Target=1000,USL=1300Mean=1196,Stddev=115.0,.,69,Exercise5,Foreachofthe4distributionsbelow,checkifyouthinkCpk1.3,.,70,Cp&CpkAssumptions,ProcessStabilityTheprocessmustbeinstatisticalcontrolRepresentativeSamplesTheobtainedsamplesarerepresentativeofthepopulation.Randomsamplingisimportantinthisregard.NormalityTheunderlyingprocessdistributionisnormalNote:Iftheassumptionsarenotmet,theprocesscapabilityindicescalculatedaremisleading&donotaccuratelyindicatethecapabilityoftheprocess.Seeyoursitestatisticianforadviceoncalculationmethodswhenassumptionsareviolated.,.,71,TestforCp&CpkAssumptions,ProcessStability(nooutliers)ScreenoutoutliersfromthedatabasebeforecomputingCpkbyusingcontrolchart.Anypointbeyondeithercontrollimitisanoutlier.Reportnumberofoutliersscreened.NormalityPlotanormalprobabilityplotofthedataoroverlayanormalcurveoverthehistogram.Normallydistributeddatawillroughlyfallonastraightline.TestfornormalitybyusingShapiro-WilkWtestinJMP,.,72,NoLargeOutliers,TheexamplebelowillustratestheCpkresultswith&without2largeoutliers.,Withoutliers:Cpk=0.52,2outlierspointsremoved:Cpk=0.88,.,73,AreDataNormallyDistributed?,Processeswithlargesystematiceffectsorothernon-normalsignatureoftenresultinanunderestimatedCpk.,.,74,Cpkvs.DPMOut-of-Spec,IfweassumethattheprocessdistributionisNormal,wecanrelateCpktotheestimateddpmofproductthatisout-of-spec.,DPM=DefectivesPerMillion(i.e.theexpectednumberofdefectivepartsorunitsofproductpermillionproduced).,.,ExamplesofDeterminingtheDPMOut-of-SpecfromCapabilityIndices,.,76,OpenthedatasetThickness.jmp,whichcontains150thicknessmeasurementstakenover30lots.Thespeclimitsforthicknessare80milsto120mils.JMPAnalysisSelectDistributionfromtheAnalyzeMenu.SelectthicknessandclickY,Columns.ClickOK.SelectCapabilityAnalysisfromthepop-upmenuintheoutputwindowofthehistogram.Enterthespeclimits.ClickOK.Note:ThetargetvalueisonlyusedforthecalculationofCpm,whichisnotdiscussedhere.SelectFitDistributionfromthepop-upmenuintheoutputwindowofthehistogram&selectNormalCurve.DoubleClickontheX-axis.Enter75fortheaxisminimum.Enter125fortheaxismaximum.Note:1.Thex-axisscaledoesnotchangeautomaticallytoshowthespeclimits.2.Multipleresponsescanbeanalyzedsimultaneouslyinthesameoutputwindow.,ComputingCapabilityIndicesusingJMP,.,77,Thegraphbesideisahistogramof150platingthicknessmeasurementstakenfromseveralproductionlots.LSL=80,USL=120Inthesummarytables,notethatthevalueofCpk=1.038agreeswiththehistogram-thisprocessisnotcapable.,%ofproductout-of-spec=0.092+0.031=0.123%=1230dpm,ComputingCapabilityIndicesusingJMP,.,78,Exercise6,Adhesivebleedouthasanupperspeclimitof500microns(nolowerspeclimit).Datacollectedfrom50lots(250partstotal)iscontainedinthefileExer6.jmpWhatistheCpkoftheprocesswithrespecttoadhesivebleedout?Whatlong-termaveragepercentageofpartsdoyouestimatewillbeout-of-specforadhesivebleedout?,.,79,IntelsGoalonProcessCapability,ThegoalforacapableprocessistohavetheCpk1.33IftheminimumCpkof1.33isnotmet,theteammustgenerateplanstoimprovetheprocesscapability