工程方法_6sigma_10測(cè)量系統(tǒng)分析

1、MSA-Measurement System Analysis (GR&R)測(cè)量系統(tǒng)分析(重復(fù)性和再現(xiàn)性)主 要 培 訓(xùn) 內(nèi) 容Learning Objectives學(xué)習(xí)目的Explain how measurement is a process解釋工序中如何進(jìn)行測(cè)量的Effects of Measurement System variability on the process variability測(cè)量系統(tǒng)誤差對(duì)工序誤差的影響B(tài)asic terms and concepts in MSAMSA的基本術(shù)語(yǔ)和內(nèi)容Analysis of Variable GR&R計(jì)量型GRR 的分析Analys

2、is of Attribute GR&R計(jì)數(shù)型GRR的分析Analysis and Application of Nested GR&R 嵌套式GRR 的分析和應(yīng)用如果從你座位前的電腦上看上面的圖片左邊的臉是生氣的臉而右邊的臉是平靜的臉但請(qǐng)你起立往后走35步突然間他們交換位置了!這個(gè)幻覺(jué)圖片是由Glasgow大學(xué)的PhillippeG.SchynsandAudeOliva所設(shè)計(jì)由此證明,我們可能從來(lái)沒(méi)看到過(guò)事情的真正面貌?？闯鰜?lái)了別忘了回到座位上，呵呵What is a Measurement System?測(cè)量系統(tǒng)是什么? 測(cè)量: 以確定實(shí)體或系統(tǒng)的量值大小為目標(biāo)的一整套作業(yè)測(cè)量系統(tǒng): 由人

3、,量具,測(cè)量方法和測(cè)量對(duì)象構(gòu)成的過(guò)程的整體測(cè)量系統(tǒng)分析: 指用統(tǒng)計(jì)學(xué)的方法來(lái)了解測(cè)量系統(tǒng)中的各個(gè)波動(dòng)源,以及它們對(duì)測(cè)量結(jié)果的影響,最后給出本測(cè)量系統(tǒng)是否合乎使用要求的明確判斷.觀測(cè)值真實(shí)值測(cè)量系統(tǒng)測(cè)量人員測(cè)量設(shè)備測(cè)量物料測(cè)量方法測(cè)量環(huán)境Verify product/ process conformity to specifications驗(yàn)證產(chǎn)品/工序和規(guī)格的一直性Assist in continuous improvement activities為連續(xù)性的改善活動(dòng)提供支持Why Worry About Measurement Variation? 為什么擔(dān)心測(cè)量系統(tǒng)誤差?Consider

4、the reasons why we analyze the measure system:想一下為何我們要分析測(cè)量系統(tǒng):How might measurementvariation affect these decisions?測(cè)量變異影響以上這些決定有多大程度?What if the amount of measurement variation is unknown如果測(cè)量誤差未知那又到何種程度?Product產(chǎn)品Measurement測(cè)量Product產(chǎn)品Measurement測(cè)量Measurement variation can make our processes LOOK wor

5、se than they are.測(cè)量誤差能使我們的工序看起來(lái)比實(shí)際更差MSA: Warm-up Exercise MSA: 熱身練習(xí)Line 2拉 2Line 1拉 1From ongoing line monitoring, we conclude that Line 1 and Line 2 are producing similar parts (same spread).從觀測(cè)結(jié)果看,我們認(rèn)定拉1和拉2生產(chǎn)相同的產(chǎn)品(相同分布)Process variability study工序方差研究s2observed (Equal for both lines)兩條拉觀測(cè)值相同Enginee

6、ring decides to perform a MSA to understand measurement variability, before proceeding with improvement plans在進(jìn)行改善計(jì)劃前,工程部決定進(jìn)行測(cè)量系統(tǒng)分析來(lái)分析測(cè)量變異,Line 拉1Line拉 2Total Variability(observed)全部變異(觀測(cè)值)Measurement Variability測(cè)量變異S2observed觀測(cè)值s2 MS 1測(cè)量系統(tǒng)!s2 MS 2測(cè)量系統(tǒng)2MSA: Warm-up Exercise MSA: 熱身練習(xí)Process variabil

7、ity study工序方差研究Questions問(wèn)題:1. Are the two lines equivalent ?兩條拉一樣嗎?2. Which measurement system is useful to detect process variability ?哪一個(gè)測(cè)量系統(tǒng)適用于檢測(cè)工序變異?3. What would you do next for process improvement for each line ?對(duì)于每一條拉的改善行動(dòng),你下一步打算做什么?Line拉 1Line拉 2Total Variability(observed)全部變異(觀測(cè)值)Measuremen

8、t Variability測(cè)量變異True Process Variability真實(shí)工序變異s2true process(真實(shí)狀況) + s2MS(測(cè)量系統(tǒng)) = s2observed(觀測(cè)的)MSA: Warm-up Exercise MSA: 熱身練習(xí)Measurement System Analysis Approach測(cè)量系統(tǒng)分析方法There are two types of measurements possible:兩種可能的測(cè)量Variable計(jì)量值Data can be described on a continuous scale數(shù)據(jù)可以用連續(xù)的標(biāo)尺描述Attribute

9、計(jì)數(shù)值Data cannot be adequately described on a continuous scale數(shù)據(jù)不能以連續(xù)的標(biāo)尺描述Pass / Fail, very low counts通過(guò)/不通過(guò),好/壞 Each must be approached differently.每一種方法必須用不同的處理方法Variable Measurement System Analysis計(jì)量值測(cè)量系統(tǒng)分析The ideal measurement system will produce “true” measurements every time it is used (Zero Bia

10、s, Zero Variance).理想的測(cè)量系統(tǒng)每次會(huì)得出”真”測(cè)量值(零偏差,零變異)Study of your measurement system will reveal the relative amount of variation in your data that results from measurement system error.研究你的測(cè)量系統(tǒng)可以揭示數(shù)據(jù)中包含的測(cè)量誤差的變異量It is also a great tool for comparing two or more measurement devices or two or more operators.

11、它可以是一個(gè)很好的工具比較兩或更多測(cè)量設(shè)備或兩或兩個(gè)以上測(cè)量員工.MSA should be used as part of the criteria for accepting a new piece of measurement equipment to manufacturing.MSA 應(yīng)該是制造業(yè)的新測(cè)量系統(tǒng)的合格接受標(biāo)準(zhǔn)It should be the basis for evaluating a measurement system which is suspect of being deficient.應(yīng)該為受懷疑的測(cè)量系統(tǒng)能力不足提供評(píng)估基礎(chǔ)It should be part

12、 of the periodic maintenance program.應(yīng)該是定期維護(hù)程序的一部分.Possible Sources of Process Variation測(cè)量誤差的可能來(lái)源To address actual process variability, variation due to the measurement system must first be identified and separated from that of the process為了準(zhǔn)確定義實(shí)際的工序變異,和測(cè)量系統(tǒng)變異,我們必須首先從工序變異中驗(yàn)證和區(qū)分它們.Observed Process Va

13、riation觀測(cè)的工序誤差A(yù)ctual Process Variation實(shí)際的工序誤差Measurement Variation測(cè)量誤差Long-term Process Variation長(zhǎng)期的工序誤差Short-term Process Variation短期的工序誤差Variation With Sample樣本間誤差Variation dueTo instrument測(cè)量設(shè)備誤差Variation dueTo Operators員工操作誤差Repeatability重復(fù)性Bias偏差Stability穩(wěn)定性L(fǎng)inearity線(xiàn)性Reproducibility再現(xiàn)性Resolutio

14、n精度MeasurementVariationHumidityCleanlinessVibrationLineVoltageVariationTemperatureFluctuationOperatorTechniqueStandardProceduresSufficientWorkTimeMaintenanceStandardCalibrationFrequencyOperatorTrainingEaseofDataEntryTool工具Environment環(huán)境Work Methods工作方法Sources of Measurement Variation測(cè)量誤差的來(lái)源WARNING敬告I

15、gnoring these variables can adversely affect the gage study.忽略這些變量會(huì)阻礙儀器研究Mechanical instability機(jī)械不穩(wěn)定性 Electrical instability 電子不穩(wěn)定性 Algorithm instability 運(yùn)算不穩(wěn)定性 Wear 磨損數(shù)據(jù)輸入不仔細(xì) 員工培訓(xùn) 校正頻率 維護(hù)標(biāo)準(zhǔn) 不足夠的工作時(shí)間 標(biāo)準(zhǔn)程序 員工技術(shù)測(cè)量誤差 濕度 清潔度 震動(dòng) 電壓變動(dòng)溫度波動(dòng)Measurement System Variability - Determined through “R&R Study”測(cè)量系統(tǒng)誤

16、差通過(guò)”重復(fù)性和再現(xiàn)性研究決定Accuracy準(zhǔn)確性Precision精確性ssstotalproductmeasurement222=+totalproductmeasurementMeasurement System Bias -Determined through “Calibration Study”測(cè)量系統(tǒng)偏差通過(guò)”校正研究”決定Averages平均值Variability誤差Effects of Measurement Error測(cè)量誤差的影響Common Terms in Variable MSA計(jì)量值測(cè)量系統(tǒng)分析的一般術(shù)語(yǔ)“Accuracy” related terms:”準(zhǔn)確

17、性”相關(guān)的術(shù)語(yǔ)Bias偏差Linearity線(xiàn)性Stability穩(wěn)定性“Precision” related terms :”精確性”相關(guān)的術(shù)語(yǔ)Repeatability重復(fù)性Reproducibility再現(xiàn)性Discrimination (Resolution)分辨力(精度)Concerned with the Mean和均值有關(guān)Concerned with the Variance和方差有關(guān)準(zhǔn)確度和精確度1)準(zhǔn)確但不精確2) 精確但不準(zhǔn)確3)既不精確也不準(zhǔn)確4)既精確又準(zhǔn)確Accuracy準(zhǔn)確性Instrument accuracy (or bias) is the differenc

18、e between the observed average value of measurements and the master value.設(shè)備精確性(或偏差)是觀測(cè)的平均值和基準(zhǔn)值之間的差別The master value is an accepted, traceable reference standard (e.g., NIST).基準(zhǔn)值(標(biāo)準(zhǔn)值)是可接受的,可追述的參考標(biāo)準(zhǔn)(Master Value基準(zhǔn)值(Reference Standard)參考值A(chǔ)verage Value平均值A(chǔ)ccuracy or Bias精度或偏差Effects of Accuracy精確度的影響Ac

19、tual processAverage實(shí)際的工序均值Measurement Bias測(cè)量偏差Observed processAverage觀測(cè)到的工序均值Effect on the process: 工序影響Shifts the mean of the distribution 分布均值的偏移Bias123全部產(chǎn)品測(cè)量系統(tǒng)Instrument 2Instrument 1Master Value(Reference Standard)AverageValueAccuracy精確度Accuracy may be affected by two different types of sources:

20、 精確度可能受兩種來(lái)源的影響:Lack of good calibration to a true standard缺乏真實(shí)標(biāo)準(zhǔn)值的良好校正Average Bias: different operators or machines are centered differently, such that their average is offset from the true value平均偏差:不同的員工或不同的機(jī)器不同地集中,這樣一來(lái)他們的均值和真實(shí)值有一定的偏移量.Average BiasBias標(biāo)準(zhǔn)值(參考標(biāo)準(zhǔn))平均值偏差設(shè)備1設(shè)備2Accuracy or Bias精度或偏差A(yù)n en

21、gineer chose five “golden units” that represented the expected range of measurements. Twelve random measurements were made on each part. Historical process variation was found to be 12 mm.一工程師選擇五個(gè)”金標(biāo)準(zhǔn)樣本”希望得出測(cè)量系統(tǒng)的偏差, 每一個(gè)樣本隨機(jī)測(cè)量12次, 歷史工序誤差是12mm.Data can be found in the 01-05.MTW file.數(shù)據(jù)在01-05文件Mean of

22、the sixty measurements 60次測(cè)量的均值 x= 5.9467 mmMean value of the five standards 5個(gè)標(biāo)準(zhǔn)值的均值 = 6 mmBias= x = 5.9467 6= 0.0533 mm= 0.444% of process variationExample 1例1A measure of the change in bias over the range of instrument capability.測(cè)量系統(tǒng)的線(xiàn)性是指在其量程范圍內(nèi),偏倚是基準(zhǔn)值的線(xiàn)性函數(shù).Linearity is an issue here線(xiàn)性是這的一個(gè)結(jié)果Lin

23、earity線(xiàn)性BiasTrue Value真實(shí)值A(chǔ)ccuracy changes over part range精度在整個(gè)范圍的變化TrueValue1真實(shí)值1TrueValue 2真實(shí)值1TrueValue 3真實(shí)值3TrueValue 4真實(shí)值4偏倚定義Linearity線(xiàn)性BiasTrue Value真實(shí)值A(chǔ)ccuracy changes over part range精度在整個(gè)范圍的變化偏倚斜率b線(xiàn)性 = 斜率b X 過(guò)程變差Std Deviation標(biāo)準(zhǔn)偏差Linearity線(xiàn)性Using the data set from Example 1,用例1的數(shù)據(jù)(01-05文件)Mas

24、terMean Error 20.49167 40.12500 60.02500 8 - 0.29167 10 - 0.61667Best Fit Line最好的擬合線(xiàn) : Mean Error 均值偏差 = 0.7367 - 0.1317 MasterLinearity線(xiàn)性 = 0.1317 12 = 1.58 mm(13.17% of process variation)Example 2例2= (Ave. of 12 measurements) - (Master Value)=(12個(gè)測(cè)量值的均值)-(標(biāo)準(zhǔn)值)Linearity線(xiàn)性Compute the accuracy and li

25、nearity for the data in Example 1, using MiniTabs Gage Linearity Study.計(jì)算在例1中數(shù)據(jù)的精度和線(xiàn)性,用MiniTab: Gage Linearity StudyExample 3例3Stat Quality Tools Gage Linearity StudyExample 3此為MiniTab13版本Gage Linearity & Accuracy Study計(jì)量?jī)x線(xiàn)性和精度研究Gage Linearity & Accuracy Study計(jì)量?jī)x線(xiàn)性和精度研究Stability穩(wěn)定性The distribution o

26、f measurements remains constant and predictable over time for both mean and standard deviation對(duì)于均值和標(biāo)準(zhǔn)偏差測(cè)量的分布隨時(shí)間保留恒定的可預(yù)測(cè)的No drifts, sudden shifts, cycles, etc無(wú)漂移,突然偏移,循環(huán),等等.Evaluated using a trend chart用一個(gè)趨勢(shì)圖評(píng)估Ensured through a regular calibration and R&R program通過(guò)定期的校正和重復(fù)性和再現(xiàn)性程序Master Value真實(shí)值(Refer

27、ence Standard)參考標(biāo)準(zhǔn)Time 2時(shí)間2Time 1時(shí)間1A measure of the change in bias over time.在一定時(shí)間內(nèi)偏差變化的測(cè)量Effects of Stability穩(wěn)定性的影響Long term observedvariabilityActual processdistributionMeas. Systemday 1Meas. Systemday 2Meas. Systemday 3Effect on the process:工序的影響Increases process variability over time.隨時(shí)間增加工序變異T

28、ime實(shí)際的工序分布測(cè)量系統(tǒng)第一天測(cè)量系統(tǒng)第二天測(cè)量系統(tǒng)第三天長(zhǎng)期觀測(cè)到的變異Precision精確度Actual processvariabilityMeasurement variabilityTotal observedvariabilityEffect on the process:工序的影響Increases process variability.增加工序變異實(shí)際的工序變異測(cè)量變異全部觀測(cè)到的變異Precision精確度Total variation in the measurement system測(cè)量系統(tǒng)的全部變異Measure of the total variation o

29、f repeated measurements重復(fù)測(cè)量的全部變異Precision equals the sum of Repeatability and Reproducibility精確度相當(dāng)于重復(fù)性和再現(xiàn)性的總和 s reproducibilityrepeatabilityproducttotal 2 2 2 2 sss + +=Precision: Repeatability精確度:重復(fù)性Repeatability重復(fù)性The inherent variability of the measurement device測(cè)量設(shè)備本身的變異Variation that occurs whe

30、n repeated measurements are made of the same variable under similar conditions:在相同的狀態(tài)下測(cè)量重復(fù)的數(shù)據(jù)時(shí)產(chǎn)生的變異: Same part 相同的產(chǎn)品 Same operator 相同的員工 Same set-up 相同的設(shè)置 Same environmental conditions 相同的環(huán)境狀況 Short-term 短期Estimated by the pooled (average) standard deviation of the distribution of repeated measureme

31、nts利用pooled (average) standard deviation 計(jì)算重復(fù)測(cè)量sss s 2 2 2 2totalproductrepeatabilityreproducibility=+ +Precision: Repeatability精確度:重復(fù)性Repeatability: the variation between successive measurements of the same part, same characteristic, by the same person using the same instrument. Also known as test

32、- retest error; used as an estimate of short-term measurement variation.重復(fù)性: 同一個(gè)操作者利用相同的設(shè)備,連續(xù)測(cè)量相同部分,相同特性,所獲得的測(cè)量值的變差.Master ValueGood RepeatabilityPoor Repeatability標(biāo)準(zhǔn)值良好的重復(fù)性差的重復(fù)性sss s 2 2 2 2totalproductrepeatabilityreproducibility=+ +Precision: Reproducibility精確度: 再現(xiàn)性Reproducibility:再現(xiàn)性 The variati

33、on that results when different conditions are used to make the same measurements 在不同狀況下做相同測(cè)量而導(dǎo)致的變異:Different operators 不同的操作者Different set-ups 不同的設(shè)置Different test units 不同的測(cè)量對(duì)象Different environmental conditions 不同的環(huán)境狀況Long-term measurement variation 長(zhǎng)期的測(cè)量變異 Estimated by the standard deviation of the

34、 averages of measurements from different measurement conditions用不同的測(cè)量狀況下的測(cè)量均值的標(biāo)準(zhǔn)差計(jì)算Inspector AMaster ValueInspector BInspector CMachine AMachine BMachine CReproducibility: The standard deviation of the averages of the measurements made by different persons, machines, tools, etc. when measuring the i

35、dentical characteristic on the same part再現(xiàn)性: 當(dāng)測(cè)量相同元件的指定特性時(shí),不同操作者,機(jī)器,工具等的測(cè)量均值的標(biāo)準(zhǔn)偏差.Precision: Reproducibility精確度: 再現(xiàn)性標(biāo)準(zhǔn)值操作者A操作者B機(jī)器A操作者C機(jī)器B機(jī)器CExample: Accuracy vs Precision練習(xí):準(zhǔn)確性和精確性Suppose we have a reference material with a true hardness of 5.0:假設(shè)我們有一個(gè)”真”強(qiáng)度值5.0的參考材料Method 1 gives the following readi

36、ngs:方法1.給出下列讀值3.8, 4.4, 4.2, 4.0Method 2 gives the following readings:方法2.給出下列讀值6.5, 4.0, 3.2, 6.3Which method is more accurate?哪種方法更準(zhǔn)確?Which method is more precise?哪種方法更精確Which method do you prefer? Why?你更愿意用哪種方法? 為何?Discrimination (Resolution)分辨力(精度)The number of decimal places that can be measure

37、d by the system. Increments of measure should be at least one-tenth of the width of the product specification or process variation.測(cè)量系統(tǒng)測(cè)量的小數(shù)點(diǎn)的位數(shù), 測(cè)量的增量至少是產(chǎn)品規(guī)格或工序總波動(dòng)的1/10.12Which ruler should be used to measure parts for the process represented by the distribution above ?哪一個(gè)尺子應(yīng)該用于測(cè)量上述工序分布?Measurement

38、 System Variance: s2meas = s2repeat + s2reprod 測(cè)量系統(tǒng)方差 Primary output of analytical Gage R&R 分析GRR的主要輸出To determine whether the measurement system is “good” or “bad” for a certain application, you need to compare the measurement variation to the product spec or the process variation決定測(cè)量系統(tǒng)是否是”好的” 或”懷的

39、”對(duì)于一定的應(yīng)用. Comparing s2meas with Tolerance:同公差比較s2meas Precision-to-Tolerance Ratio (P/T)精確度對(duì)于公差比 Comparing s2meas with Process Variation (P/TV):同工序變異比較s2meas % Repeatability and Reproducibility (%R&R) 重復(fù)性和再現(xiàn)性的百分比Discrimination Index 分辨指數(shù)Measurement System Metrics測(cè)量系統(tǒng)組成Precision to Tolerance Ratio精確度

40、對(duì)于公差比Addresses what percent of the Tolerance is taken up by measurement error.表明測(cè)量誤差占了公差的百分比5.15 smeas represents 99% of all measurements5.15 smeas 代表 99% 的測(cè)量Best case: 10% Marginally Acceptable: 30%最好: 10% 最低可接受: 30% Includes both repeatability and reproducibility包括重復(fù)性和再現(xiàn)性Usually expressed as perce

41、nt通常用百分比表達(dá)Tolerance = USL - LSLP/T Ratio精確度對(duì)于公差比P/TV%R&R(or % Contribution)Addresses what percent of the Total Variation is due to measurement error表明測(cè)量誤差占了全部方差的百分比P/TV: Best case: 10% Acceptable: 30%P/TV: 最好: 10% 可接受: Make Patterned Data Simple Set of Numbers (for each input)Step 4: Ask the first o

42、perator to measure all the samples once in random order. Blind sampling, in which the operator does not know the identity of each part should be used to reduce human bias.步驟4: 讓第一個(gè)員工隨機(jī)測(cè)量所有的樣本一次, 模糊樣本,讓操作者不知道哪一個(gè)樣本以減少人為誤差.Step 5: Have the second operator measure all the samples once in random order an

43、d continue until all operators have measured the samples once (this is trial 1)步驟5.: 讓第二個(gè)操作者隨機(jī)測(cè)量所有的樣本,一直到所有的操作者測(cè)量完所有的樣本一次.(這是第一次)Step 6: Repeat steps 4 & 5 for the required number of trials. It is best if these measurements can be done over several days.步驟6.重復(fù)步驟4.和5完成需要的次數(shù).最好是這些測(cè)量能在這幾天內(nèi)完成.Step 7: En

44、ter the data and tolerance information into Minitab 步驟7: 輸入數(shù)據(jù)和公差在MiniTab中.Stat Quality Tools Gage R&R StudyStat Quality Tools Gage Run ChartStep 8: Analyze the results by assessing the quality of the measurement system based on the guidelines on the following page. Determine follow-up actions.步驟8: 根

45、據(jù)下面的指引通過(guò)評(píng)估測(cè)量的質(zhì)量來(lái)分析結(jié)果.決定下列步驟: The Method - Calculating Gage Capability方法計(jì)算測(cè)量?jī)x器能力MSA Sample Guidelines測(cè)量系統(tǒng)分析 樣本指引Usually 10 samples, 2-4 operators (if several operators use the gauge), 2-3 trials用10個(gè)樣本2-4個(gè)操作者(如果有幾個(gè)操作者使用這個(gè)儀器),2-3次重復(fù)Depending on the purpose of the study 1 or more gages will be included由

46、研究1或更多的儀器所決定.In general select enough samples so that number of samples x number of operators/gages 15一般來(lái)說(shuō)選擇足夠的樣本,樣本數(shù)量和操作者成績(jī)和儀器比大于15SAMPLE SELECTION 樣本選擇Option 1: if process variability is unknown, the samples selected should be representative of the normal process/product variation (to get TV)選擇1:

47、如工序變異不知,樣本選擇應(yīng)該代表正常的工序/產(chǎn)品變異(取得全部變異)Option 2: if process variability is known, the samples selected should uniformly span beyond the width of the specs選擇2: 如工序變異已知,則樣本選擇應(yīng)均勻分布在規(guī)格范圍內(nèi)外(不應(yīng)超過(guò)規(guī)格過(guò)多).*Example: Minitab例: MiniTabStep 1: Randomly select 10 samples. In addition, identify the operators who use thi

48、s instrument daily步驟1.隨機(jī)選取10個(gè)樣本,并且確認(rèn)每日操作此設(shè)備的員工(Parts 1 through 10 were collected and three operators were identified).部分1到10被選取并三個(gè)確認(rèn)的操作者Step 2: Calibrate the gage or verify the last calibration date is valid步驟2.校正測(cè)量?jī)x器或確認(rèn)最近的一次校正是否有效Use Calc Make Patterned Data Simple Set of NumbersStep 3: Setup the M

49、initab data collection sheet for the R&R study. Create the R&R data collection sheet for 10 parts each measured 2 times by 3 operators步驟3.準(zhǔn)備好MiniTab數(shù)據(jù)收集sheet做GR&R研究,每10單位測(cè)量2次被3個(gè)操作者, Column headings: 列題頭Column 1: Part ID (1-10)Column 2: Operator (1-3)Column 3: Trial (1-2)Column 4: Measurement(s)Step

50、4: Ask the first operator to measure all the samples once in random order. Blind sampling, in which the operator does not know the identity of each part should be used to reduce human bias.步驟4: 讓第一個(gè)員工隨機(jī)測(cè)量所有的樣本一次, 模糊樣本,讓操作者不知道哪一個(gè)樣本以減少人為誤差.Step 5: Have the second operator measure all the samples once

51、in random order and continue until all operators have measured the samples once (this is trial 1).步驟5.: 讓第二個(gè)操作者隨機(jī)測(cè)量所有的樣本,一直到所有的操作者測(cè)量完所有的樣本一次.(這是第一次)Step 6: Repeat steps 4 & 5 for the required number of trials.(We assume the above steps were executed. See GageR&R.mtw.) 步驟6.重復(fù)步驟4.和5完成需要的次數(shù)(我們假設(shè)上述步驟已完成

52、,看文件01-06)Example - Gage R&R using Minitab例子: 儀器重復(fù)性和再現(xiàn)性研究用MiniTabANOVA method is preferred.選擇ANOVA方法.Step 7: Enter the data and tolerance information into Minitab.步驟7: 輸入數(shù)據(jù)和公差在MintTab Stat Quality Tools Gage R&R StudyFN: GageR&R.mtwEnter Gage Info and Options (see next page)Example - Gage R&R using

53、Minitab例子: 儀器重復(fù)性和再現(xiàn)性研究用MiniTabtotalmeastotalmeasTVPssss=22/Step 7: Enter the data and tolerance information into Minitab. 步驟7: 輸入數(shù)據(jù)和公差在MintTabStat Quality Tools Gage R&R StudyGage Info (see below) & OptionsLSLUSLLSLUSLTPMSMS-=-=ss*15.5*15.5/2Note: The denominator of the P/TV calculation can be estim

54、ated from the samples used in the study or from a historical estimate.注意:P/TV 計(jì)算的分母是由所研究的樣本得來(lái)的,或從歷史數(shù)據(jù)而來(lái).Example - Gage R&R using Minitab例子: 儀器重復(fù)性和再現(xiàn)性研究用MiniTabGage R&R Study - ANOVA MethodANOVA Table With Operator*Part InteractionSource DF SS MS F P Parts 9 2.05871 0.228745 39.7178 0.00000Operators

55、2 0.04800 0.024000 4.1672 0.03256Oper*Part 18 0.10367 0.005759 4.4588 0.00016Repeatability 30 0.03875 0.001292 Total 59 2.24912 Gage R&RSource VarComp StdDev 5.15*SigmaTotal Gage R&R 0.004437 0.066615 0.34306 Repeatability 0.001292 0.035940 0.18509 Reproducibility 0.003146 0.056088 0.28885 Operator

56、0.000912 0.030200 0.15553 Oper*Part 0.002234 0.047263 0.24340 Part-To-Part 0.037164 0.192781 0.99282 Total Variation 0.041602 0.203965 1.05042 Source %Contribution %Study Var %ToleranceTotal Gage R&R 10.67 32.66 22.87 Repeatability 3.10 17.62 12.34 Reproducibility 7.56 27.50 19.26 Operator 2.19 14.8

57、1 10.37 Oper*Part 5.37 23.17 16.23 Part-To-Part 89.33 94.52 66.19 Total Variation 100.00 100.00 70.03 Number of Distinct Categories = 4 Session Window Output:Graphical Output:Answers! But what do they mean?Lets investigate each section one at a time.Gage R&R Output重復(fù)性和再現(xiàn)性輸出Gage R&R Study - ANOVA Met

58、hodANOVA Table With Operator*Part InteractionSource DF SS MS F p-valueParts 9 2.05871 0.228745 39.7179 0.00000Operators 2 0.04800 0.024000 4.1672 0.03256Oper*Part 18 0.10367 0.005759 4.4588 0.00016Repeatability 30 0.03875 0.001292 Total 59 2.24913 The ANOVA table provides insight into the significan

59、ce of the various possible sources of errorANOVA方法揭示了不同可能原因錯(cuò)誤的重要度However, ANOVA will be covered in the Module 3.Gage R&R, ANOVA TableGauge R&R Study - ANOVA MethodSource Var Comp Std Dev 95% Conf Int 5.15*Sigma Total Gauge R&R 0.004437 0.066615 (0.0597,0.2250) 0.34306 Repeatability 0.001292 0.035940

60、 (0.0287,0.0480) 0.18509 Reproducibility 0.003146 0.056088 ( *, *) 0.28885 Operator 0.000912 0.030200 (0.0000,0.2169) 0.15553 Oper*Part 0.002234 0.047263 (0.0254,0.0800) 0.24340 Part-To-Part 0.037164 0.192781 (0.1098,0.3345) 0.99282 Total Variation 0.041602 0.203965 1.05042 Variance due to the measu