【導(dǎo)讀】MEASUREMENT. SYSTEM. ANALYSIS. Contents:. 1)THEAVERAGE-RANGEMETHOD. 2)THEANOVAMETHOD. 1)SHORTMETHOD. 4)LONGMETHOD. 4)CONCLUSION. MSA. Requirement. VariableGage. AttributeGage(Go/No-goGage). GeneralProcess. Value. MeasurementProcess. 2σ。efrom?efrom?Stability. efrom?measurementsystem.Linearity. efrom?

　　

【正文】 lumn totals sum to the same value for the overall total, and the overall total matches the number of cases in the original data set. Step 4 Compute the total number of agreements by summing the values in the diagonal cells of the table. Σa = 53+ 89 = 142 Step 5 Compute the expected frequency for the number of agreements that would have been expected by chance for each coding category. ef = = = Repeat the formula for other cell, we got other expected count (show in the table 3). row total * col total overall total 59 * 55 150 Step 6 Compute the sum of the expected frequencies of agreement by chance. Σef = + = Step 7 Compute Kappa K = = = Step 8 Evaluate Kappa A general rule of thumb is that values of kappa greater than indicate good to excellent agreement。 values less than indicate poor agreement. Repeat above step, we can got following kappa measures for the appraisers: Table 4 Analysis Techniques: Attribute Gage Study Σ aΣ ef NΣ ef Ka p p a A B C A B C Table 4 Using the same steps to calculated the kappa measure to determine the agreement of each appraiser to the reference decision: Table 5 Total summary on Table 6: Analysis Techniques: Attribute Gage Study A B C K app a Table 5 S ourc e A p p ra i s e r A A p p ra i s e r B A p p ra i s e r CT ot a l Ins p e c t e d 50 50 50 M a t c he d 44 46 43M i x e d 6 4 795%U CI % % %Ca l c ul a t e d S c ore 88% 92% 86%95%L CI % 81% % T ot a l Ins p e c t e d 50 i n A g re e m e nt 4295%U CI % Ca l c ul a t e d S c ore 84%95%L CI %%S c ore vs A t t ri but eA n n e B e n C a t h y7 58 59 5A p p r a i s e rPercentW i t h i n A p p r a i s e rA n n e B e n C a t h y7 58 59 5A p p r a i s e rPercentA p p r a i s e r v s S t a n d a r dA s s e s s m e n t A g r e e m e n tD a t e o f s t u d y :R e p o r t e d b y :N a m e o f p r o d u c t :M i s c :[ , ] 9 5 . 0 % C IP e r c e n tAnalysis Techniques: Attribute Gage Study Analysis Techniques: Attribute Gage Study The AIAG MSA reference manual edition 3 provides acceptability criteria for each appraisers results: Definition: False Alarm – The number of times of which the operator (s) identify a good sample as a bad one. Miss – The number of times of which the operators identify a bad sample as a good one. D eci si on M easu r em ent S y st e m E f f ect i venes s Mi ss R at e Fal se A l arm R at e A cce pt abl e f or appr ai ser ≥ 90% ≤ 2% ≤ 5% M ar gi nal l y accept abl e f or t he app r ai ser m a y need i m pr o v e m ent ≥ 80% ≤ 5% ≤ 10% U nac cept abl e f or t he appr ai ser needs i m pr o v e m ent s 80% 5% 10% Number of correct decisions Total opportunities for a decision Effectiveness = Analysis Techniques: Attribute Gage Study Number of False Alarm Total opportunities for a decision False Alarm Rate = Number of False Alarm Total opportunities for a decision Miss Rate = So summarizing all the information of the example with this table: Table 7 F als e A l ar m R at eA 88% 4% 8%B 92% 6% 2%C 86% 8% 14%E f f e c t i ve n e s s M i s s R at eAnalysis Techniques: Attribute Gage Study Conclusion: The measurement system was acceptable with appraiser B, marginal with appraiser A, and unacceptable for C. So we shall determine if there is a misunderstanding with appraiser C that requires further training and then need to redo MSA. The final decision criteria should be based on the impact to the remaining process and final customer. Generally, the measurement system is acceptable if all 3 factors are acceptable or marginal. Minitab also can perform attribute gage analysis, but it didn’t declare the acceptability criteria, so it is not recognized by QS9000 standard. Analysis Techniques: Attribute Gage Study ? Signal Detection Theory is to determine an approximation of the width of the region II area so as to calculate the measurement system GRamp。R. Also used filler gage as example to perform Signal Detection approach. The tolerance is ~. The process needs to take a random sample of 50 parts from the practical process and use 3 appraisers who make 3 measurements on each part, and then got following table: Table 8 II II Target I I III USL LSL P ar t A 1 A 2 A 3 B1 B2 B3 C 1 C 2 C 3 R e fe r V al u e C od e1 0 0 0 0 0 0 0 0 0 2 1 1 1 1 1 1 1 1 1 +3 1 1 1 1 1 1 1 1 1 +4 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 6 1 0 1 0 1 1 1 0 0 x7 1 1 1 1 1 1 1 0 1 x8 0 0 0 0 0 0 0 0 0 9 1 1 1 1 1 1 1 1 1 +10 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 12 1 1 1 1 1 1 1 1 1 +13 1 1 1 1 1 1 1 1 1 +14 1 1 1 1 1 1 1 1 1 +15 1 1 1 1 1 1 1 1 1 +16 1 1 1 1 1 1 1 1 1 +17 1 1 1 1 1 1 1 1 1 +18 1 1 1 1 1 1 1 1 1 +19 1 1 1 1 1 1 1 1 1 +20 1 1 0 1 1 1 1 0 0 x21 0 0 0 0 0 0 0 0 0 22 0 0 0 0 0 0 0 0 0 23 0 0 1 0 1 0 1 1 0 x24 1 1 1 1 1 1 1 1 1 +Table 8 Signal Detection Table for Filler Gage 25 1 1 1 1 1 1 1 1 1 +26 1 1 1 1 1 1 1 1 1 +27 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 29 0 0 0 0 0 1 0 0 0 x30 1 1 1 1 1 1 1 1 1 +31 1 1 1 1 1 1 1 1 1 +32 1 1 1 1 1 1 1 1 1 +33 0 0 1 0 0 1 0 1 1 x34 1 1 1 1 1 1 1 1 1