MINITAB之製程能力分析易騰涂順章1 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)2 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)3 Capability Analysis (Normal)•該命令會劃出帶理論正態曲線的直方圖，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4 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5 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6 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7 STEP1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•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8 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9 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10 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MINITAB>STAT>QUALITY TOOL•>CAPABILITY ANALYSIS (NORMAL)11 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12 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 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 1097.16 98.26 97.59 100.09 99.78 13 輸入數據14 執行能力分析15 輸入選項16 選擇標准差的估計方法17 選項的輸入18 以Cpk, Ppk結果的輸出19 以Zbench方式輸出20 結果說明•請學員按此圖形來說明該製程狀況21 Capability Analysis (Between/Within)22 Capability Analysis (Weibull)•此項的分析是用在當制程不是呈現正態分佈時所使用。
因為如果制程不是正態分佈硬用正態分佈來分析時，容易產生誤差，所以此時可以使用韋氏分佈來進行分析，會更貼近真實現像23 練習•請使用同前之數據來進行分析•上規格：103•下規格：97•規格中心：10024 選韋氏分佈25 輸入相關參數26 填入選項要求27 結果圖形28 比較二者有何差異•此二項誰更適合來解釋制程狀況•如果你是制程工程師你應如何抉擇29 正態分佈適用性的判定•可以使用–Stat>basic statistic>normality 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30 數據調整31 選擇執行項目32 填寫選項33 結果輸出34 結果輸出(加標0.5概率)35 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)•當非正態分佈時則可以使用–Capability Analysis (Weibull)36 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, PPK37 練習•請以前面的數據來進行相應的Capability Sixpack (Normal)練習38 選capability six pack (normal)39 輸入各項參數40 選定判異准則41 選擇標准差估計方法42 考慮可選擇項43 結果輸出44 Capability 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, PPK45 同前練習及結果46 Capability Sixpack (Weibull)•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, PPK47 結果輸出48 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。
49 示例•數據在excel檔案中50 選二項分佈制程能力51 填好各項的參數52 選好控制圖的判異准則53 填入選擇項54 結果及輸出55 結果解釋•請針對前圖進行相應的各項解釋56 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57 示例•數據在excel檔案中58 選卜氏分佈制程能力59 填好各項的參數60 選好控制圖的判異准則61 填入選擇項62 結果及輸出63 結果解釋•請針對前圖進行相應的各項解釋64 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 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. 65 •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, 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 the 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 0.9974.66 •PPL is the ratio of X0.5 - LSL to X0.5 - X0.0013. PPU is the ratio of USL - X0.5 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 similar 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.67 •The PPM < LSL (1.03209) indicates that for machine 1, 1 out of 1 million is expected to fall below the lower specification limit of 27 oz. The PPM > USL (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 capability 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.• 68 •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.• 69 。