质量管理工具复件 doe1-3

1、Purposes and practices ofDesign of ExperimentsA Method to: describe predict and controlvariables in a process in order to understand and improve the process or product.In order to improve a process or system one must first understand it. If it is not understood, then any change made to it is tamperi

2、ng. The effect then is almost always detrimental to the quality of the process and product.Purposes of DOETo gain knowledge about theeffects of variables (factors) in a processIdentify primary and secondary variablesDetermine optimum levels of variablesDetermine interactions of variablesPurposes of

3、DOEIdentify key factors to design (orre-design) a process or product for better quality/efficiency.Identify critical design parameters (1.2)Provides a base to monitor measurements and testsCan help in Failure Modes and Effects Analysis (FMEA)Practices of DOEUses statistical principles to obtainaccur

4、ate resultsYields more information than one-at-a- time experimentsStatistically valid design gives statistically valid resultsUses confidence testing to determine significance of effectsPractices of DOELeads to good engineeringpractice such as:Highly structured and thoughtful design and implementati

5、on encourages good design practice.Provides a base to mathematically model a process using response surface methodology. (session 2)Promotes systematic and logical testing that validates and verifies intuition and experience.Encourages good decisions through documentation and data analysis.Practices

6、 of DOEDOE builds on basic quality toolsDepends on an in-control processControl chartsHistograms Flow ChartsIdentification of factors Cause and Effect diagrams Pareto charts and Check sheetsMonitoring of changes made by DOE Control Charts Scatter plotsPurposes and practices ofDesign of ExperimentsSu

7、mmaryDOE provides an mechanism foractive change in the improvement process.A very effective high level tool that can be used in detail operations, sequential processes or overall systems.DOE in Manufacturing:Injection Molding ExampleFactorLow LevelHigh LevelFiber Length(FL).254 mm3.175mmFiber Concen

8、tration (FC)20%40%ProcessTemperature (PT)277oC304oCMold Thickness (MT)1.587 mm3.175 mmTensile test Pareto ChartEffects of Molding Variables on Tensile Strength282624222018161412108642099%95%Interactions plotWhat are the interactions that would effect the process? Example of fiber length and fiber co

9、ncentration(MPa)155FC 40%125FC 20%9565FC 20%FC 40%353.175.254Fiber LengthInteractions PlotWhat are the interactions that would effect the process? Example of temperature and mold thickness.(MPa)900700500300277C304CProcess TemperatureMT 1.587MT 3.175MT 1.587MT 3.175Interactions plot - 3-wayexampleWha

10、t are the interactions that would effect the process? Example of all three factors.400883Note: Values are in MPa421304842PT3796963.175MT2774349311.587.254 FL 3.175DOE in ManufacturingComposites injection moldingexampleBetter able to “manage” the process for cost, timeand qualityBetter able to predic

11、t the effects of adjustments of the processCan better meet customer requirementsDOE in manufacturingThe process in now easier tomanage in the system for:Production amountsProduction timesInventory/material requiredRelationship to other operationsIdentify resourcesnecessary for successful DOERemember

12、 DOE is a prevention cost activityMore difficult to measure savingsExternal FailureInternal FailureTraining and planning are critical and on- goingAppraisal PreventionTime in processIdentify DOE resourceneedsManagement support necessaryTime and money for trainingNeed management perspective to see im

13、pact on the whole systemDowntime, if necessary of processMaterial/product costs (25-30% on experiment #1)Knowledge and expertise Statisticians (if any) can be of help Experts involved in, or related to, the process being testedIdentify DOE resourceneedsA method to help determine timeand material res

14、ource needsWhat product/process will be targeted and why?How much time/material will be necessary to conduct experiment #1? (then multiply by four)What will be the effect on production?When will be the best time to conduct the experiment Production schedule & personnel External conditions that may a

15、ffect resultsHow much time to analyze and document the experimentAdvantages andlimitations of DOEAdvantagesStatistical foundation yields a lot of information for relatively low costProvides main, secondary and interaction effects of factors being testedBasic designs can be conducted and evaluated wi

16、thout significant statistical knowledge or expertiseMuch more information than obtained from one-at-a-time experimentationPro-active tool for directing improvementsAdvantages andlimitations of DOELimitationsNot valid if process is not in-control or statistically stableRequires more analytical skill

17、than basic quality toolsSometimes is used on processes when simpler tools would sufficeCare must be take not to confuse results, for example: Maintain homogeneity in material External conditions should be as stable as possibleRequires good planning and documentationIdentifying specific goalsfor DOEA

18、sk “Why am I using this tool onthis problem?” This will help identify the desired response variable (the goal) for the experiment and ensure DOE should be used.Ask “How will improving thedesired response variable improve the process?” Validates the need for conducting the experimentIdentifying speci

19、fic goalsfor DOEAsk “How will improving thetargeted process affect the system?” Protects against sub-optimization of a process at the expense of the systemAsk “Can I maintain constantexternal conditions?” Helps to prevent introduction of special causes that would corrupt the analysis If the external

20、 conditions cannot be maintained constant then they can be planned forIdentifying specific goalsfor DOEAsk “What are the main factors tobe considered in this experiment?” Using a Cause and effect diagram will help answer this question All who are part of the process (management to line people) shoul

21、d be involvedAsk “Do I have baselineinformation for a benchmark?” If no baseline has been obtained this should be the first goal of data collectionEnd Section OneMain Tab Page for SectionTwoIntroduction to theexperimental methodTerminology -2Rk-p2Number of levels of each factorKNumber of factors in

22、each experimentPFactors generated in the base designk-pNumber of factors assigned to interaction2k-pNumber of runs2-pThe fraction of the full factorial 2k2pNumber of main effects and interactions confounded togetherRDesign resolutionIII =main effects confounded with 2-factor interactions IV=2 factor

23、 interaction confounded with each other V=2 factor interactions are clearIntroduction to the experimentalmethodThe Factorial PatternRow12A+-B+C+D+ k=134+-+ k=25678+-+-+-+ k=3910111213141516+-+-+-+-+-+-+- k=4Introduction to theexperimental methodHow the factors and interactionsare tested (refer to th

24、e factorial pattern on the previous page)Factor A against desired response in rows 1 & 2 (also rows 3 & 4)Rows 1 & 8 together show effect of C on effect of B and effect of A (3-way)Rows 1-4 show the change of A at high B and at low B (2-way)Software does all this automaticallyIntroduction to theexpe

25、rimental methodSummaryFull factorials cover full design spaceFull factorials are easy to lay out - repeating pattern in standard orderMultiple factors are tested and interactionsDesign of the DOECause and effect diagram (right side)Review section 1item 5Determine how to measure the response variable

26、Design of the DOEScreening designs (more later)Cause and effect diagram (left side)Historical data as baselineDesign of the DOEBlocking Planning for known variation (noise) that may be inherent in the experimental sample. For example: two replications from same lot 1/2 of design in lot A and 1/2 in

27、lot BRandomizationTo avoid time trends and other variation not knownNever run in sequence orderAvoids the effects of hidden variable Validates statistical conclusionsUse tables or numbers from hatDesign of the DOEReplicationA replication of a run is an independent and random application of the run i

28、ncluding set up.Measure experimental variabilityImprove estimate of effectsDetermine if change in factor levels is special (induced) or common cause variationA repeat is application of a run without a new setup.Data CollectionStrive for homogenous materialSame lots etc.Reduce effects of time through

29、 randomization or new setup (example: chemical bath degradation)Use blocking and randomization to minimize external effectsData CollectionRecord keepingEmphasize integrity and accuracy of documentationResisting estimating (let the data perform its function)Note special conditions in the data collect

30、ion sheetAlways include as much information as possible - name, time, date, location, operator, shift, etc.Data CollectionResults will be no betterthan the quality of data you obtain and record.Complete accuracy andintegrity in the data is criticalStatistical tests for DOEvariables and factorsCalcul

31、ating effects A effect is the difference in the averages Main effects E(A)=A+ -A- Interaction effects E(AB)=1/2(A+ -A-)B+ - (A+ -A-)B-Statistical tests for DOEvariables and factorsStandard deviation of experimentruns Se=(Si2/k)Standard deviation of effectsSeff=Se(4/N) where N in the total number of

32、trialsT-statisticusing t-tables (appendix) and degrees of freedom wheredegrees of freedomd.f.=(# of observations per run-1) X (# of runs) See example with in class experimentStatistical tests for DOEvariables and factorsCharting the resultsPareto charts of results with significancelimits121110987654

33、321099%95% Statistical tests for DOEvariables and factorsInteraction plots(MPa)1,200FC 40%parallel lines mean no interaction900 FC 20%600 FC 40%FC 20%300.2543.175Fiber Length(MPa)155FC 40%normal lines mean strong interaction125FC 20%9565FC 20%FC 40%353.175.254Fiber LengthStatistical tests for DOEvar

34、iables and factorsCube Plots400883Note: Values are in MPa421304842Shows the effects at planes and corners of the factorialPT3796963.175MT2774349311.587.254 FL 3.175In class experimentDetermine the effects of:Heat treatment VendorSizeon the durability of paper clips.Will use 3 factors at 2 levels asd

35、efined by the experiment sheetIn class experimentGroups of four people with thefollowing tasks assignedTest engineer Runs the test (bends the clips)Analyst Leads the team in calculating resultsRecorder Ensures all data is properly recordedLead engineer Ensures runs and treatments are performed in pr

36、oper order and formIn class experimentPaper Clip experiment - 23Std. OrderVendorHeat TreatmentSize1NoestingYes#12AccoYes#13NoestingNo#14AccoNo#15NoestingYesJumbo6AccoYesJumbo7NoestingNoJumbo8AccoNoJumboIn class ExperimentDate:Paper Clip Durability experimentVendorHeat TreatmentSize+ = Noesting- = Ac

37、co+ = 20 min 400oF- = none+ = #1- = JumboRun #Seq. #VendorHeatSize# of bendsComments12345678910111213141516In class experimentTime order plot of data12345678910111213141516In class experimentSummary Table for experimental resultsStd. OrderVendorHeatSizeDataboth runsAverageRangeRange21+,2-+,3+-+,4-+,

38、5+-,6-+-,7+-,8-,In class experimentWorksheet for computing effectsNoise = the standard error of an effectStd. OrderMeanVSHVSVHSHVSHAve. Bends1+2+-+-+-3+-+-+-4+-+-+5+-+-6+-+-+-+7+-+8+-+-3(+)3(-)3(+)+3(-)3(+)-3(-)3(+)-3(-)/4effect/noiseIn class experimentCalculate effectsCalculate t-ratiosConstruct Pa

39、reto Chart of effects*Construct Cube plot*Construct interaction charts* * See appendix for chart blanksEnd Section TwoMain Tab Page for SectionThreeReview of Taguchi lossand DOE applicationsDeviation from target is lossLoss can be calculate for point or sampleLoss at a point is L(X) = k(x-m)2Loss of

40、 the sample set is L=ks2 (in $ per unit)k=A/2 (A is the cost of failure at the spec limit, 2=squared distance from target to failure value)x=measured value m=target values=standard deviation of the sampleLSLTargetUSLReview of Taguchi lossand DOE applicationsDOE builds a model to identifyboth product

41、 and process target valuesRobust design (product orprocess) will include DOE experiments.Testing existing target values onproducts or process using DOE and historical dataYour own DOE projectDetermine baseline for the projectIs the process in-control? If not then special causes must be eliminated be

42、fore proceedingDo you have historical data available? If not then determine data collection method and collect data If the process is in-control then proceed. If not see step 1.Determine factors andlevels for your DOEUse a cause and effect diagram with others in involved to determine factors to be testedUse only three factorsMethodManMachineMaterialDete