MINITAB之制程能力分析精选课件.ppt

1、MINITAB之製程能力分析易騰涂順章製程能力之分類計量型(基於正態分佈)計數型(基於二項分佈)計數型(基於卜氏項分佈)MINITAB 能力分析的選項(計量型)Capability Analysis(Normal)Capability Analysis(Between/Within)Capability Analysis(Weibull)Capability Sixpack(Normal)Capability Sixpack(Between/Within)Capability Sixpack(Weibull)Capability Analysis(Normal)該命令會劃出帶理論正態曲線的直方

2、圖，這可直觀評估數據的正態性。輸出報告中還包含過程能力統計表，包括子組內和總體能力統計。Capability Analysis(Between/Within)該命令會劃出帶理論正態曲線的直方圖，可以直觀評估數據的正態性。該命令適用於子組間存在較變差的場合。輸出報告中還包含過程能力統計表，包括子組間子組內和總體能力統計。Capability Analysis(Weibull)該命會會劃出帶韋伯曲線的直方圖，這可直觀評估數據是否服從韋伯分布。輸出報告中還包含總體過程總能力統計製程能力分析做法決定Y特性收集Y特性數據輸入MINITAB數據表進行分析結果說明STEP1決定Y特性決定Y特性收集Y特性數據

3、輸入MINITAB數據表進行分析結果說明Y特性一般是指客戶所關心所重視的特性。Y要先能量化，儘量以定量數據為主。Y要事先了解其規格界限，是單邊規格，還是雙邊規格。目標值是在中心，或則不在中心測量系統的分析要先做好。STEP2決定Y特性決定Y特性收集Y特性數據輸入MINITAB數據表進行分析結果說明在收集Y特性時要注意層別和分組。各項的數據要按時間順序做好相應的整理STEP3決定Y特性決定Y特性收集Y特性數據輸入MINITAB數據表進行分析結果說明將數據輸入MINTAB中，或則在EXCEL中都可以。STEP4決定Y特性決定Y特性收集Y特性數據輸入MINITAB數據表進行分析結果說明利用MINIT

4、ABSTATQUALITY TOOLCAPABILITY ANALYSIS(NORMAL)STEP5決定Y特性決定Y特性收集Y特性數據輸入MINITAB數據表進行分析結果說明利用MINITAB的各項圖形來進行結果說明練習樣本X1X2X3X4X5199.70 98.72 100.24 101.28 101.20 299.32 100.97 100.87 99.24 98.21 399.89 99.83 101.48 99.56 100.90 499.15 99.71 99.17 99.30 98.80 599.66 100.80 101.06 101.16 100.45 697.74 98.82

5、 99.24 98.64 98.73 7101.18 100.24 99.62 99.33 99.91 8101.54 100.96 100.62 100.67 100.49 9101.49 100.67 99.36 100.38 102.10 輸入數據執行能力分析輸入選項選擇標准差的估計方法選項的輸入以Cpk,Ppk結果的輸出103.5102.5101.5100.599.598.597.596.5TargetUSLLSLProcess Capability Analysis for X1-X5PPM TotalPPM USLPPM USLPPM USLPPM USLPPM USLPPM US

6、LPPM USLPPM USLPPM USLPPM USLPPM USLPPM basic statisticnormality test 但數據要放到同一個column中，所以必須針對前面的數據進行一下處理數據調整選擇執行項目填寫選項結果輸出P-Value:0.515A-Squared:0.324Anderson-Darling Normality TestN:50StDev:1.12483Average:99.9056102101100999897.999.99.95.80.50.20.05.01.001Probabilityx1Normal Probability Plot結果輸出(加標

7、0.5概率)P-Value:0.515A-Squared:0.324Anderson-Darling Normality TestN:50StDev:1.12483Average:99.905610210110099989799.90560.50000.999.99.95.80.50.20.05.01.001Probabilityx1Normal Probability Plot計量型製程能力分析總結 一般的正態分佈使用 Capability Analysis(Normal)如果是正態分佈且其組內和組間差異較大時可用 Capability Analysis(Between/Within)當非正

8、態分佈時則可以使用 Capability Analysis(Weibull)Capability Sixpack(Normal)複合了以下的六個圖形 Xbar R 原始數據分佈 直方圖 正態分佈檢定 CPK,PPK練習 請以前面的數據來進行相應的Capability Sixpack(Normal)練習選capability six pack(normal)輸入各項參數選定判異准則選擇標准差估計方法考慮可選擇項結果輸出109876543210101.5100.599.598.5Xbar and R ChartSubgrMean11Mean=99.91UCL=101.1LCL=98.744.53.

9、01.50.0RangeR=2.025UCL=4.282LCL=0109876543210Last 10 Subgroups102.5101.099.598.0Subgroup NumberValues103T 97Capability PlotProcess ToleranceIIIIIIIIISpecificationsWithinOverall10210098Normal Prob Plot10210098Capability HistogramWithinStDev:Cp:Cpk:0.8706231.151.11OverallStDev:Pp:Ppk:Cpm:1.130590.880.

10、860.89Process Capability Sixpack for X1-X5Capability Sixpack(Between/Within)複合了以下的六個圖形 Xbar R 原始數據分佈 直方圖 正態分佈檢定 CPK,PPK同前練習及結果103.5101.098.596.0I-MR-R ChartMeanMean=99.91UCL=102.9LCL=96.923210Mov.RangeR=1.124UCL=3.672LCL=01098765432104.53.01.50.0SubgroupRangeR=2.025UCL=4.282LCL=0103T 97Capability Pl

11、otProcess ToleranceIIIIIIIIISpecificationsBetween/WithinOverall103.0100.598.0Normal Prob Plot10210098Capability Histogram0.91720.87061.26461.13060.790.770.880.860.89StDevsBetw:Within:Total:Overall:CapabilityCp:Cpk:Pp:Ppk:Cpm:Process Capability Sixpack for X1-X5Capability Sixpack(Weibull)複合了以下的六個圖形 X

12、bar R 原始數據分佈 直方圖 正態分佈檢定 CPK,PPK結果輸出109876543210101.5100.599.598.5Xbar and R ChartSubgrMeansMean=99.91UCL=101.1LCL=98.744.53.01.50.0RangesR=2.025UCL=4.282LCL=0109876543210Last 10 Subgroups102.5101.099.598.0Subgroup NumberValues103 97Overall(LT)Shape:102.700Scale:100.439Pp:0.74Ppk:0.64Capability PlotP

13、rocess ToleranceSpecificationsIIIIII10210110099989796Weibull Prob Plot10210098Capability HistogramProcess Capability Sixpack for X1-X5二項分佈制程能力分析 二項分佈只適合用在 好，不好 過，不過 好，壞 不可以用在 0,1,2,3等二項以的選擇，此種狀況必須使用卜氏分佈。示例 數據在excel檔案中選二項分佈制程能力填好各項的參數選好控制圖的判異准則填入選擇項結果及輸出25201510500.060.050.040.030.020.010.00Sample Nu

14、mberProportionP=0.01658UCL=0.04367LCL=02520151052.12.01.91.81.71.61.51.41.3Sample Number%Defective4.53.01.50.0Target25020015010076543210%DefectiveSample SizeBinomial Process Capability Report for 不良數Summary StatsCumulative%DefectiveDist of%DefectiveP ChartRate of Defectives(denotes 95%C.I.)Average P

15、:%Defective:Target:PPM Def.:Process Z:0.01658031.6580165802.130(0.0132,0.0206)(1.32,2.06)(13168,20594)(2.042,2.221)結果解釋 請針對前圖進行相應的各項解釋卜氏分佈制程能力分析 卜分佈只適合用在 計數型，有二個以上的選擇時 例如可以用在 外觀檢驗，但非關鍵項部份 0,1,2,3等二項以的選擇，此種狀況必須使用卜氏分佈。示例 數據在excel檔案中選卜氏分佈制程能力填好各項的參數選好控制圖的判異准則填入選擇項結果及輸出25201510500.080.070.060.050.040.03

16、0.020.010.00Sample NumberSample CountU=0.02342UCL=0.05588LCL=02520151050.0280.0260.0240.0220.020Sample NumberDPU0.0450.0300.0150.000Target2502001501000.040.030.020.01DPUSample SizePoisson Process Capability Report for 不良數_1Summary StatsCumulative DPUDist of DPUU ChartDefect Rate(denotes 95%C.I.)Mean

17、 DPU:Min DPU:Max DPU:Targ DPU:0.02341970.0120.040(0.0193011,0.0281569)結果解釋 請針對前圖進行相應的各項解釋Example of Capability Analysis for Multiple Variables(Nonnormal)1 Open the worksheet MNCAPA.MTW.2 Choose Stat Quality Tools Capability Analysis Multiple Variable(Nonnormal).3 In Variables,enter Weight.4 Check BY

18、 variables and enter Machine.4 In Fit data with,choose Distribution and then select Largest extreme value.5 In Lower spec,enter 27.In Upper spec,enter 35.6 Click OK.The probability plot confirms that the data follows largest extreme value distribution.For machine 1,AD=0.335 and P 0.25.For machine 2,

19、AD=0.341 and P 0.25.The capability statistics are based on the 0.5,99.87 and 0.13 percentiles denoted as X0.5,X0.9987,and X0.0013.The percentiles are calculated using the parameter estimates for the largest extreme value distribution.Pp is defined as the ratio of the specification range(USL-LSL)to t

20、he potential process range(X0.9987-X0.0013).Pp for machine 1 and machine two are 0.84 and 0.90 respectively,indicating that the probability that the process produces conforming frozen food packets is slightly less than 0.9974.PPL is the ratio of X0.5-LSL to X0.5-X0.0013.PPU is the ratio of USL-X0.5

21、to X0.9987-X0.5.For machine 1,PPL=1.33 and PPU=0.66,indicating that more than 0.13 percent pf the process output is more than the upper specification limit.This also indicates that the process has median close to the lower specification limit.This is also evident in the histogram.Machine 2 show simi

22、lar results.Ppk is the minimum of PPU and PPL.For both machines,high value of Pp and low value of Ppk indicate that the process median is off the specification midpoint.This also indicates that more than 0.13 percent of the process output is outside at least one of the specification limits.The PPM U

23、SL(10904)indicates that for machine 1,10904 out of 1 million are expected to exceed the upper specification limit of 35 oz.Machine two show similar results.Industry guidelines determine whether the process is capable.A generally accepted minimum value for the indices is 1.33.For both machines the ca

24、pability indices are lower than 1.33.The process tends to put more food in a package than the upper limit.The manufacturer needs to take immediate steps to improve the process.CCpk is a measure of potential capability.It is identical to the Cpk index except that,instead of being centered at the process mean all the time,it is centered at the target when given or the midpoint of the specification limits when the specification limits are given.CCpk is precisely Cpk when one of the specification limits and the target is not given.