6 Sigma Yellow Belt_单元五 量测系统分析

1、6 Sigma Yellow Belt單元五 量測系統分析 (MSA)1 M S A 量 測 觀 念 要 求 標 準 M S A 種 類 分 析 方 法 運 用 範 圍 前 置 工 作 評 鑑 程 序 隨 堂 測 驗 23456789101112131415161718192021製程分析作業流程檢測作業改善檢測系統更修分析消除變異製程改善或?GR&R?Ca/Cp/Cpk?Ca/Cp/Cpk?起始檢測系統追溯校正MSA短期 SPC長期 SPC製程品質再提昇YYYYNNNN2223 Variables (連續型) 平均值全距法 ANOVA Attributes (離散型) 檢驗員本身及檢驗員間一

2、致性判定 有“大師”或“專家”標準 Kappa 統計分析M S A 種類24MSA Variables(連續型) 2526272829數 據 資 料 表30GR&R 分析表格31GR&R 分析表格5.15*3233343536373839變異的組成R ChartXbar ChartPart 分析OP分析PartXOP交互作用Gage R&R (ANOVA) for Response404142434445MSA Attributes(離散型) 46量測方法 實驗:檢驗員以隨機順序方式進行量測 分析工具: Minitab R14 Attribute Agreement Analysis 推論:

3、檢驗員本身(重複性) 不同檢驗員間(再現性) 與大師或“專家”標準比較(再現性) Kappa 統計分析47 檢驗員:2-3 位 樣本:建議至少30個 (合格/不合格/兩者之間) 試驗次數:每個樣本重複量測2-3次 量測工具: 主觀判斷 Go/No Go 限度樣本/ 標準品 量測方法 48實驗步驟 步驟1: 第一位檢驗員以隨機方式針對每個樣品 判定合格/不合格或缺點數個數 步驟2: 第二位及第三位檢驗員分別重複同樣方 式進行 步驟3: 重複1-2 步驟,直到完成所須量測次數 49分 析Data / Code / Text to NumericNG/ G 轉為數值格式NG: 1G : 2開啟檔案:

4、 MSA Attribute .mpj選擇Result編碼為數字格式欄位名稱將數據編碼轉為數值格式50Stat / Quality Tools / Attribute Agreement Analysis分 析選擇 PartOperator編碼後的欄位名稱51檢驗員1 : 第一次量測與第二次量測不一致兩位檢驗員判定不一致檢驗員2 : 第一次量測與第二次量測不一致分 析問題所在: 非水平的直線52重複性: 檢驗員本身分 析95% CI: The confidence interval represents the range into which you can expect the perce

5、nt matched to fall53重複性: 檢驗員本身顯示信賴區間分 析54再現性: 不同檢驗員間各檢驗員間判定一致性分 析55有“大師”或“專家”標準分 析56分 析選擇所有檢驗員欄位輸入檢驗員數量及每個樣本量測次數選擇大師或專家標準欄位選擇 result 顯示 kappaStat / Quality Tools / Attribute Agreement Analysis57重複性: 檢驗員本身分 析推論:兩位檢驗員在10個樣本中,有8個判定一致58再現性: 不同檢驗員間分 析推論:兩位檢驗員在10個樣本中,有8個判定一致59各檢驗員與專家比較分 析推論:每個檢驗員和專家判定結果各有

6、7個一致60所有檢驗員與專家比較分 析推論:所有檢驗員和專家判定結果有5個一致61分 析62Kappa 統計分析:StatGuide Kappa ( 摘錄自 Minitab R14 線上協助)The ratio of the proportion of times that the appraisers agree (corrected for chance agreement) to the maximum proportion of times that appraisers could agree (corrected for chance agreement). When you ha

7、ve a known standard, kappa is the average across trials.If kappa =1, then there is perfect agreement . If kappa =0, then the agreement is the same as would be expected by chance. Negative values occur when agreement is weaker than expected by chance , but this rarely happens. The higher the kappa va

8、lue, the stronger the agreement. 63Kappa 統計分析:StatGuide Kappa Statistics ( 摘錄自 Minitab R14 線上協助) If each appraiser provides two or more ratings for the same unit, you can assess the consistency of each appraisers ratings across the trials. If Kappa = 1, then there is perfect agreement. If Kappa = 0,

9、 the agreement is the same as would be expected by chance. The stronger the agreement, the higher the value of Kappa. Negative values occur when agreement is weaker than expected by chance, but this rarely happens. Depending on the application, Kappa less than 0.7 indicates that the measurement syst

10、em needs improvement. Kappa values greater than 0.9 are considered excellent. Compare the Kappa statistics for each Response and for Overall. Are appraisers having difficulty with a particular response? For the fabric data, all Kappa statistics are greater than 0.7. The appraisers rating consistency

11、 is within acceptable limits. Use the p-values to choose between two opposing hypotheses, based on your sample data: Ho: The agreement within appraiser is due to chance H1:The agreement within appraiser is not due to chance The p-value provides the likelihood of obtaining your sample, with its Kappa

12、 statistic, if the null hypothesis (Ho) is true. If the p-value is less than or equal to a predetermined level of significance (a-level), then you reject the null hypothesis and claim support for the alternative hypothesis. For the fabric data, with a = 0.05, for all appraisers and all responses, p

13、= 0.000, so you reject the null hypothesis. The rating agreements for print quality are significantly different from those that would be achieved by chance. The within-appraiser statistics do not compare the appraisers ratings to the known standard. Although the appraisers ratings may be consistent,

14、 they are not necessarily correct.64Kappa 統計分析 用來評估各個檢驗員在各次試驗間的一致性Kappa = 1, 判定結果完全一致Kappa = 0, 判定結果一致性的情況只是碰巧發生一致性越強, Kappa 值越高當一致性比碰巧發生的機率還低時, Kappa 為負值, 但這很少發生；隨應用範圍不同, 一般來說 Kappa 小於 0.7 表示測量系統需要改善Kappa 值大於 0.9 表示測量系統極佳65Kappa 統計分析(假設檢定) H0 (虛無假設): 檢驗員本身判定結果一致情況是 由於碰巧發生H1 (對立假設): 檢驗員本身判定結果一致情況由 於非碰巧發生 If P value -Level (一般= 0.05), Acc H066Kappa 統計分析 檢驗員判定結果一致性,檢驗員1 較佳Kappa值0.7,表示量測系統須改善重複性: 檢驗員本身P Value 0.05 ,Reject H067Kappa 統計分析 Kappa值0.7,表示量測系統須改善判定通過與無通過Kappa值相同再現性: 不同檢驗員間P Value 0.05 ,Reject H068Kappa 統計分析 Kappa值0.7,表示量測系統須改善各檢驗員與專