Measurement System Analysis - A Detailed Study

Measurement System Analysis

1. Measurement System Analysis By Mr. D Roy Email : [email protected] Ph no – 9204058839 07.06.2021

2. Content 1. MSA Definition 2. MSA Parameter 3. MSA Tools & Methods 4. MSA Plan & Execution

3. P art - 1 MSA basics

4. Measurement : Expressing a parameter in terms of number or decision, Eg 60 mm, 100 bar, Good, Not ok, etc. Measurement System : Collection of instruments / gauges, standards, operations, methods, fixtures, software, personnel, environment and assumptions used for assessment of the feature characteristic being measured ( Measurement Systems are complete Process, not only restricted to gauges ) Analysis : Expressing data in manner so that meaningful conclusions can be drawn, Eg. Pareto, histogram, charts, etc. MSA : Measurement System Analysis is a quantitative evaluation of the tools and pKrocess used in making discrete or variable observations Part -1 : MSA basics What is MSA : Measurement System Analysis

5. Decision depends on Data which is captured through measurement system. MSA – Helps analyzing goodness of measurement system . Why MSA? Quality of Decision Quality of Data Quality of Measurement System MSA Part -1 : MSA basics

6. Some Consequences of Error in Measurement Systems Saying good product as bad and bad as good . Stable process treated as unstable or/and capable process as incapable, working unnecessarily improving the process . Wrong conclusion from DOE/regression analysis . Wrong solution for your problem . Why MSA ? Part -1 : MSA basics

7. MSA Process Flow Control Plans, List of Instruments, new product Preparation/Updating of MSA Plan MSA Manual, Competency On concept of MS and Statistical studies Identification of statistical studies to meet the requirement of the measurement and decide the frequency MSA Plan MSA Plan, WI & MSA manual Perform MSA studies Study Report Initiate & implement corrective action I nput Output Activities Prepare summary Report & submit to customer If required Y E S Analyze the data using graphical tools / brainstorming & find out causes for variation It is acceptable ? NO Part -1 : MSA basics

8. Calibration Vs MSA Calibration Conducted in c controlled environment . Conducted using masters. Conducted by qualified/ trained people. Checks accuracy only. M S A Conducted in actual working condition . Conducted using actual products/Parts Conducted by actual users (operators/inspectors ) Checks accuracy and precision Can calibration replace MSA? Part -1 : MSA basics

9. Quantify variation in the measurement system (MS). Decide whether MS is capable for the study . Ensure stability of the measurement system, Can we detect process improvement if and when it happens ? Validate measurement system . Compare consistency between inspectors . Identifying causes for variation in MS & Initiating appropriate actions to reduce variation . Objectives of MSA Part -1 : MSA basics

10. P art - 2 MSA parameters

11. Parameters V ariation. Accuracy and precision. Discrimination. Uncertainty. Bias. Uncertainty. Stability or drift. Repeatability. Reproducibility. Part -2 : MSA parameters

12. V ari a tion Variation is the Law of Nature. Variation cannot be eliminated; it can only be reduced. Goal is to achieve maximum possible reduction in variation. Part -2 : MSA parameters

13. V ari a tion Part -2 : MSA parameters

14. V ari a tion Part -2 : MSA parameters

15. Manufacturing Process Variation, MPV T o t al V ar i a ti o n To have a good measurement system, the Measurement System Variation should be minimum Measurement System Variation, MSV Variation within different parts under the measurement due to variation in process Variation due to process of measurement method of instrument. This can be due to error by the operator, use of wrong type of instrument or problem in instrument Part -2 : MSA parameters

16. Total Variation Actual Process Variation Measurement Variation Long-term Process Variation Variation within Sample Short-term Process Variation Variation due To instrument Variation due To appraisers Linearity Stability Bias Repeatability Reproducibility Part -2 : MSA parameters

17. Measurements Mechanical Integrity We a r Electrical Instabili Algorithm Instabilit Materials Cleanliness Te m per a tu re Dim e n s ion Weight Corrosion Hardness Conductivity Density Men Pr o cedure Fatigue Attention Calibration Error Interpretation Speed coordination Vision Temp Fulxctuation Line Voltage Variati Cl ean l i nes s Operator Technique Standard Procedure Sufficient Work time Maintenance Standard Calibration Frequency Operator Training Ease of use Humidity Wear St a bi l i ty R e s o l ut i on Calibration Precision Design t e m per a t u r e Cleanliness M S V Environment Methods Machines Sources of Measurement System Variation (MSV) Part -2 : MSA parameters

18. Accuracy & Precision Not precise Not Accurate Precise Accu r ate Not precise Accurate Precise Not Accurate A ccuracy (Deals with Location) Closeness to reference or master value Required where two or more MS measuring a same characteristic Same parameters are checked at Suppliers or Customer end Captured by Bias, Linearity, Stability P recision (Deals with Spread) Ability of the MS to Reproduce or Duplicate readings Required where MS is repeatedly used to assess and adjust the process In process inspection as per control plan . Captured by Repeatability & r ep r od u cibi l ity Part -2 : MSA parameters

19. A P A P Accuracy - Closeness of mean to target Precision - Adherence to mean A P A P Part -2 : MSA parameters

20. Ability of the Measurement System to detect small changes in measured values. Measurement system is not fit for controlling process if it cannot detect process variation effectively. T raditional approach Least count = 1/10 th of Tolerance (This is used when we want to know that product is ok or not ok.) B est-in-class approach Least Count = 1/10 th of Process variation (This is used when we want to know about process variation.) Disc rimination ( or resolution or least count) Part -2 : MSA parameters

21. Uncertainty True value is unknown and CAN’T be known. We can only know the reference value. The difference between the true value and the measured value is the error . We can only go closer to the true value. Uncertainty is a band within which true value is expected to lie. It is measured at defined confidence level (generally 95%) Error Uncertainty X X-U X+U Measured True Part -2 : MSA parameters

22. Error D = C measured – C true C true C measured C true -U C true + U True value Measured value Range of uncertainty Measurand,C Uncertainty Part -2 : MSA parameters

23. Uncertainty Random Uncertainties R esult s from the randomness of measuring instruments . They can be dealt with by making repeated measurements and averaging. One can calculate the standard deviation of the data to estimate the uncertainty. Systematic Uncertainties R esult s from a flaw or limitation in the instrument or measurement technique. Systematic uncertainties will always have the same sign.For example, if a meter stick is too short, it will always produce results that are too long. Part -2 : MSA parameters

24. Uncertainty of measurement Standard uncertainty Extended uncertainty Type A Type B U where we need not to give Up where K = 2 -3 Up(p is the confidence level of probability) Uncertainty Part -2 : MSA parameters

25. Bias Difference between the average of measurements and the reference value A systematic error component of the measurement system BIAS = X bar – Ref. Value Measured value with regular in s t r ume nt Measured value with master instrument -Slip gauge, or -Part measurement on CMM, etc It is an instrument error (10% Acceptable) Applied where you have your own designed gauge For Vernier & micrometer instruments Bias study not required True V a l ue Bias O b s e r v e d Average Part -2 : MSA parameters

26. Linearity The change in Bias over the normal operating range Defined as the maximum deviation from the linear characteristics as a percent of full scale output. It is normally desirable that the output reading of an instrument is directly proportional to the quantity being measured. Part -2 : MSA parameters

27. Stability or drift The change in Bias over time Also known as “Drift” Used to determine calibration frequency Part -2 : MSA parameters

28. Repeatability Variation in measurement obtained with one measuring instrument when used several times by an appraiser while measuring the identical characteristic on the same part. The variation in successive (Short Term) trails under fixed and defined conditions of measurement. Commonly referred to as Equipment Variation (EV), Instrument (gauge) capability. Indicates within-system variation. Part -2 : MSA parameters

29. Repeatability Example: Appraiser A Appraiser B Appraiser C Average for Appraiser A Average for Appraiser B Average for Appraiser C Repeatability -example Part -2 : MSA parameters

30. Repeatability Example: Appraiser A Appraiser B Appraiser C Average for Appraiser A Average for Appraiser B Average for Appraiser C Repeatability -example Part -2 : MSA parameters

31. Variation in the average of the measurements made by different appraisers using the same gauge when measuring a characteristic on one part . For product and process qualification, error may be appraiser, environment (time), or method . Commonly referred to as Appraiser Variation (AV). Indicates between-system (conditions) variation Reproducibility Reproducibility Appraiser Part -2 : MSA parameters

32. Repeatability (Equipment Variation) Reproducibility (Appraiser Variation) Different Appraiser Same equipment Same Parts Repeatability vs Reproducibility R&R (Meas.System )  2   2   2 Repeatability Reproducibility Two Components One Appraiser One Equipment Same part Several trials Part -2 : MSA parameters

33. Systematic error Total error Random error Trueness Accuracy Precision Bias Measurement of uncertainty Standard deviation Repeatability / Within lab reproducibility / Reproducibility Type errors Quantitative expression of performance characteristics Performance characteristics Measurement terminology Part -2 : MSA parameters

34. P art - 3 MSA tools and methods

35. “MSA” (R&R)? Calibration? St a bil i ty? Linearity? Bias? Resolution? Steps in Measurement System Analysis Part -3 : MSA tools and methods

36. Data Qualitative Quantitative A t tr i b u t e V ar i able MSA Methods For Attributes When MSA to be done for a Gauge which is used for inspection of a variable parameter Snap Gauge, Limit Gauge, Any special gauge, etc Signal Detection method When MSA to be done for Inspection method of a parameter which cannot be measured Dent , Crack, Fouling, etc Visual inspection method Kappa method

37. For Variables Bias study Linearity study Stability study R & R (Range Method) GRR (X bar - R Method) ANOVA MSA Methods For Attributes Signal Detection Cross Tabulation (Kappa) Visual inspection study Gauge Performance Curve Part -3 : MSA tools and methods

38. P art - 3 MSA tools and methods – Variables , Gauge R&R method

39. Steps of performing MSA Select 3 appraisers. Arrange 10 samples. 1 st appraiser to take readings of all 10 samples. 2 nd appraiser then takes reading of 10 samples. 3 rd appraiser next takes readings of 10 samples. Follow steps 3 – 5, 3 times; total 90 reading to populate the data table. Calculate EV and AV Calculate GRR wrt Total variation (TV) and Tolerance (Tol) both. Plot Average graph and Range graph Part -3 : MSA tools and methods - GRR (X bar - R Method)

40. Steps for calculating MSA results Specification: USL: LSL: To l : 0.0000 Gauge ID: Date: APPRAISER TRIAL PART AVERAGE 1 2 3 4 5 6 7 8 9 10 Appraiser 1 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! # D I V / ! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! X a = # D I V / ! Range 0.0000 0.0000 0.0000 0.0000 . 000 0.0000 0.0000 0.0000 0.0000 0.0000 R a = . 000 Appraiser 2 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! # D I V / ! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! X b = # D I V / ! Range 0.0000 0.0000 0.0000 0.0000 . 000 0.0000 0.0000 0.0000 0.0000 0.0000 R b = . 000 Appraiser 3 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! # D I V / ! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! X c = # D I V / ! Range 0.0000 0.0000 0.0000 0.0000 . 000 0.0000 0.0000 0.0000 0.0000 0.0000 R c = . 000 Part Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! X p = # D I V / ! R p = # D I V / ! Average of Range = (R a + R b + R c / No. of Appraiser) R = . 000 Range of Average = (Max. X - Min. X) X DIFF = # D I V / ! Upper Control Limit for Range Chart UCLr = R x D4 . 000 Part -3 : MSA tools and methods - GRR (X bar - R Method)

41. From Data Sheet R = . 008 3 X diff = 0.0024 Repeatability (Euipment Variation) R p = 0.1434 % Repeatability K 1 = 1/d 2 * (at m = no of trials, g = no of part x no of appraisers = greater than 15) Reproducibility (Appraiser Variation) % Reproducibility = AV [( X diff x K 2 ) 2 - (EV 2 / nr)] % AV = 100 [ AV / TV] = 1.9303 = 0.0009 % AV = 100x[AV/(TOL/6)] = 1.315 Change value of K2 as per your case n = No of Parts = 10.0000 r = No of Trials = 3.0000 K 2 = 1/d 2 * (at m = no of appraisers, g = 1) Repeatability & Reproducibility (R & R) GR & R = (EV 2 + AV 2 ) 11.0139 = 0.0050 7.50 Part Variation ( PV ) % Repeatability & Reproducibility (R & R) % R & R = 10 [ R& R / TV ] = % R & R = 10 0 x G R&R / ( T O L / 6 ) = % Part Variation ( PV ) Appraisers 2 . 000 3 . 000 K 2 . 707 1 . 523 1 Parts K3 2.0000 0.7071 3.0000 0.5231 4.0000 0.4467 5.0000 0.4030 6.0000 0.3742 7.0000 0.3534 8.0000 0.3375 9.0000 0.3249 10.0000 0.3146 EV = R x K 1 Trials K 1 % E V = 100 [ EV / TV ] = 10.8434 = 0.0049 2.0000 0.8862 3.0000 0.5908 % E V = 100x[EV/(TOL/6)] = 7.3850 P V = Rp x K 3 % P V = 100 [ PV / TV ] = 99.3916 = 0.0451 % P V = 100 x PV/(TOL/6) = 67.69 Total Variation ( TV ) % TV = 100 x TV/(TOL/6) = 68.11 TV = = GRR 2 + P 0.0454 V 2 N u m b e ndc r of distinct = = Data Categories 1.41 [ PV / R&R ] 12.7241 Data Catego ries Steps for calculating MSA results Part -3 : MSA tools and methods - GRR (X bar - R Method)

42. More than 50% points in Average graph should be outside control limits. Average graph reflects the Measurement Capability of the Measurement System. Range graph reflects the Measurement Consistency of the Measurement System. All points in range graph should remain within control limit. NDC reflects no of discrete categories permissible in Measurement System. 42 Steps for interpreting MSA results Part -3 : MSA tools and methods - GRR (X bar - R Method)

43. 1. AVERAGE CHART X p = X dbar 40.855 A2 = 1.023 for n=3, 1.88 for n=2 (n=number of appraisers) R bar Range of average 0.005 0.010 0.003 0.007 0.003 0.000 0.007 0.000 0.000 0.010 0.010 X Bar 1 2 3 4 5 6 7 8 9 10 D Sarkar 40.843 40.840 40.847 40.853 40.850 40.863 40.870 40.860 40.850 40.880 S K Sinha 40.840 40.840 40.840 40.850 40.850 40.870 40.870 40.860 40.860 40.870 D Rakshit 40.850 40.837 40.840 40.850 40.850 40.867 40.870 40.860 40.850 40.880 UCL 40.860 40.860 40.860 40.860 40.860 40.860 40.860 40.860 40.860 40.860 LCL 40.850 40.850 40.850 40.850 40.850 40.850 40.850 40.850 40.850 40.850 2 3 4 5 6 7 8 9 10 Average Chart 40.890 40.880 40.870 40.860 40.850 40.840 40.830 40.820 40.810 1 D Sarkar S K Sinha D Rakshit UCL LCL Steps for interpreting MSA results 43 Part -3 : MSA tools and methods - GRR (X bar - R Method)

44. GRR – Average Chart Interpretations Condition Interpretation Action Less than 50% of readings are out of control limits Measurement system is not adequate enough to capture process variation, or Parts does not represent expected process variation Improve discrimination of the measurement system, or Select parts representing entire process variation In this example, 22 out of 30 points are outside the control limit Since this is more than half of total points, the conclusion is that the measurement system is adequate to detect part-to-part variations . More than 50% points outside control limit indicates that MS variation is much smaller as compared to part variations, hence MS is capable of detecting part-to- part variations Part -3 : MSA tools and methods - GRR (X bar - R Method)

45. 2.Range chart Range 1 2 3 4 5 6 7 8 9 10 D Sarkar 0.010 0.000 0.010 0.010 0.000 0.010 0.000 0.000 0.000 0.000 S K Sinha 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 D Rakshit 0.000 0.010 0.000 0.000 0.000 0.010 0.000 0.000 0.000 0.000 UCL 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 . 012 . 010 . 008 . 006 . 004 . 002 . 000 1 2 3 4 5 6 7 8 9 10 Range Chart D Sarkar S K Sinha D Rakshit UCL Steps for interpreting MSA results Purpose of range chart is to identify whether measurement process is under control (free from special cause) Part -3 : MSA tools and methods - GRR (X bar - R Method)

46. GRR – Range Chart Interpretations Condition Interpretation Action 1 or more point of any appraiser out of UCLr There was a special cause while taking reading Remove these readings and take same reading again from same part & appraiser and recalculate More than 1 point of only one appraiser out of UCLr His method is different from others Remove these readings. Train appraiser on method of measurement & take readings again 1 or more than one point of all appraisers out of UCLr Measurement System is sensitive to appraisers skill Check why measurement is so sensitive stop further studies before taking action on sensitivity In one part all appraisers points are out of UCLr Part is deformed or Damaged Remove all the reading for particular part and recalculate or replace the part with new part & take readings Part -3 : MSA tools and methods - GRR (X bar - R Method)

47. GRR-Acceptance Guideline % R&R Value Decision < 10% of TOL or TV Gauge is Capable 10 – 30% of TOL or TV Acceptable subject to analysis & justification w.r.t. application, cost of repair & criticality 30% of TOL or TV Measurement system need improvement / corrective action Number of distinct data categories should also be checked when doing SPC, ndc = 1.41 [PV / R&R] > 5 (best = 10) (this means R&R should always be less that 28% of PV) < 2, inadequate to provide data for study = 2, it is equivalent to a go/nogo gauge

48. Product Control Measurement is used for deciding product acceptance/rejection TOL/6 Process Control Measurement is used to find variation in parts (variation in process, identifying special cause , SPC application) TV or Process Standard Deviation (If process variation is known ) GRR % through TV or TOL/6 Part -3 : MSA tools and methods - GRR (X bar - R Method)

49. When Repeatability > Reproducibility Instrument needs maintenance Redesign gage for more rigidity Improve clamping or location of gauging Excessive within-part variation Identify the right cause & solution When Reproducibility > Repeatability Appraisers needs training on better way of using the gauge Needs better operational definition Incremental divisions on instrument are not readable Need fixture to provide consistency in gauge use Part -3 : MSA tools and methods - GRR (X bar - R Method)

50. Relation of R & R with Cp, Cpk Actual Cp/Cpk is always more than Observed C p /C pk Part -3 : MSA tools and methods - GRR (X bar - R Method)

51. P art - 3 MSA tools and methods - Attributes

52. For Variables Bias study Linearity study Stability study R & R (Range Method) GRR (X bar - R Method) ANOVA MSA Methods For Attributes Signal Detection Cross Tabulation (Kappa) Visual inspection study Gauge Performance Curve MSA tools and methods - Attributes

53. Steps of performing MSA Select 3 appraisers. Take 50 parts randomly from the process covering the entire variation (ensure at least 20% of the parts are defective). Measure all the samples with correct measuring instrument to collect the reference data for each sample. Conduct trial runs as per nomenclature of 3 appraisers; perform 3 trials for each operator. Note the readings in table. Populate the table with 450 data in total and perform calculations as per below method. MSA tools and methods - Attributes

54. LS L U S L I Reducing variation is goal of SPC Reducing variation is goal of MSA Bad product is always rejected Gray area, Some time good is called bad and bad is called good Good product is always accepted II III II I Signal Detection is used for both Repeatability & Reproducibility Steps for calculating MSA results MSA tools and methods - Attributes

55. Steps for calculating MSA results 1. After collection of all the data assign a code for each row i.e. considering 9 observations of each row(3 of each appraiser) as per below nomenclature: Arrange the reference value & codes in descending orders Calculate top width d1 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) Calculate top width d2 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) Calculate average width AW = d1+d2/2 Calculate % R & R = (AW/Tolerance)*100 + When the row has only 1 - When the row has only x When the row has both 1 and MSA tools and methods - Attributes

56. Reference value Varies from : 0.400 to 0.600 + When the row has only 1 - When the row has only X When the row has both 1 & Tolerance 0.1 Top width = d1 = 0.566152 - 0.542704 0.023448 Bottom width = d2 = 0.470832 - 0.446697 0.024135 Average width = AW = (d1+d2)/2 0.023792 % R&R = (AW/Tolerance)X100 23.79% Decision Remarks 0 called Right Decision, Effective MS 1 called 1 0 called 1 Miss Alarm, Consumer’s risk,  1 called False Alarm, Producer’s risk,  < 10 % Acceptable < 30 % Conditionally Acceptable > 30 % Needs improvement Steps for interpreting MSA results MSA tools and methods - Attributes

57. For Variables Bias study Linearity study Stability study R & R (Range Method) GRR (X bar - R Method) ANOVA MSA Methods For Attributes Signal Detection Cross Tabulation (Kappa) Visual inspection study Gauge Performance Curve MSA tools and methods - Attributes

58. Steps of performing MSA Take 50 parts randomly from the process covering the entire variation (ensure at least 20% of the parts are defective Get measurements on the parts by the operators as 1 (for OK) and 0 (for NOT OK) decisions Similar to X bar R method perform three trial runs for each set of samples for each operator. Total 450 data to populate the table. Get these parts measured / decided by a MASTER (an experienced) person for the results to be used as REFERENCE Compare each trial of each inspector with the another inspector for their decision Complete the cross tabulation table, shown in next slide Calculate Kappa for A vs B, A vs C, B vs C A vs Ref, B vs Ref, C vs Ref MSA tools and methods - Attributes

59. There are 34 times where A-1 = 1 and B-1 = 1 (that is, of the 50 parts checked there were 34 matches by A and B on their FIRST check) Steps for calculating MSA results MSA tools and methods - Attributes

60. There are 32 times where A-2 = 1 and B-2 = 1 (that is, of the 50 parts checked there were 32 matches by A and B on their second check ) Steps for calculating MSA results MSA tools and methods - Attributes

61. There are 31 times where A-3 = 1 and B-3 = 1 (that is, of the 50 parts checked there were 31 matches by A and B on their THIRD check) Total : where A-x = 1 and B-x = 1 = 34+32+31 = 97 Steps for calculating MSA results MSA tools and methods - Attributes

62. Count & Expected Count A*B Cross Tabulation B Total 1 A Count 44 6 50 Expected Count 15.7 34.3 50 1 Count 3 97 100 Expected Count 31.3 68.7 100 Total Count 47 103 150 Expected Count 47 103 150 Expected Count = (Column Total x Row Total ) / Grand Total For A=1 & B=1 Column Total Row Total Grand Total = 103 = 100 = 150 Hence, Expected count = (103 x 100)/150 = 68.7 Steps for calculating MSA results MSA tools and methods - Attributes

63. A*B Cross Tabulation B Total 1 A Count 44 6 50 Expected Count 15.7 34.3 50 1 Count 3 97 100 Expected Count 31.3 68.7 100 Total Count 47 103 150 Expected Count 47 103 150 Where p o = sum of observed proportions in the diagonal cells (left to right direction) p = sum of expected proportions in the e diagonal cells (left to right direction) Steps for calculating MSA results MSA tools and methods - Attributes

64. Effectiveness Decision More than 90 % Acceptable for the appraiser More than 80% Marginally acceptable for the appraiser Less than 80 % Unacceptable for the appraiser-Need improvement Miss Rate (Consumer’s Risk) Max 2 % False Alarm rate (Producer’s Risk) Max 5 % Steps for interpreting MSA results MSA tools and methods - Attributes

65. For Variables Bias study Linearity study Stability study R & R (Range Method) GRR (X bar - R Method) ANOVA MSA Methods For Attributes Signal Detection Cross Tabulation (Kappa) Visual inspection study Gauge Performance Curve MSA tools and methods - Attributes

66. 100% subjective inspection is not 100% effective 200% inspection is less effective than 100% (no ownership, conflict, multiply individual effectiveness) Rate of improvement noticed will be less than actual improvement for subjective inspections There are more chances of mismatch in acceptance criteria between customer & supplier Limi t a tion s : MSA tools and methods - Attributes

67. Collect min 20 samples covering good, bad (include marginal one which is part of process) Decide the reference value-It should be inline with internal / external customer requirement. Team should come with common consensus on reference value Identify the parts with numbers Ask a operator who is regularly checking these product to separate good and bad parts Record his decision about every part as good and bad Repeat step 4 and 5 with 2-3 operators for at 2-3 times Calculate Effectiveness of inspection, miss & false alarm Decide whether measurement system is accepted Steps of performing MSA MSA tools and methods - Attributes

68. Srl APPRAISER:A APPRAISER:B APPRAISER:C T ri a ls 1 T ri a ls 2 T ri a ls 3 T r a ils 1 T r a ils 2 T r a ils 3 T ri a ls 1 T ri a ls 2 T ri a ls 3 1 G G G G G G G G G 2 G G B G G G G G G 3 G B G G G B G G G 4 B B B B B B B B B 5 B B B B B B B B B 6 G G G G G G G G G 7 G G G G G G G G G 8 G B G G B B G G B 9 B B B B B B B B B 10 G G G G G G G G G 11 G G G G G G G G G 12 B G G G G G G G G 13 B B B B B B G G B 14 B B B B B B B G B 15 B B B B B B B B B 16 G G G G G G G G G 17 G G G G G G G G G 18 B B B B B B B B B 19 G G G G G G G G G 20 G G G G G G G G G G=Good; B=Bad Reference G B Appraiser G Correct Decis i on Miss Alarm B False Alarm Correct Decis i on Steps for calculating MSA results MSA tools and methods - Attributes

69. Ref G B G A B 33 2 3 22 Number of samples (N)=20 Number of trials (R)=3 Number of Good samples (NG)=12 Number of BAD samples (NB)=8 Srl DESCRIPTION A B C 1 Number of miss alarm (Nm) 02 01 05 2 Number of false alarm (Nf) 03 01 02 3 Effectiveness of inspection = No of good decisions / Total 55/60=0.92 58/60=0.97 53/60=0.88 4 Probability of miss P(MISS) = No. miss / No. of opportunities = Nm / (NBxR) 02/(8x3)=0.08 01/(8x3)=0.04 05/(8x3)=0.20 5 Probability of false alarm P(FA) = No of false alarm / No of Opportunities for false alarm = Nf/ (NGxR) 03/(12x3)=0.08 01/(12x3)=0.03 02/(12x3)=0.06 Ref G B G B B 35 1 1 23 Ref G B G C B 34 5 2 19 Miss Rate (Consumer’s Risk) Max 2 % False Alarm R ate (Producer’s Risk) Max 5 % Steps for calculating MSA results MSA tools and methods - Attributes

70. Effectiveness (E) 0.9 : accepted 0.8-0.9 : conditionally accepted < 0.8 : unacceptable Probability of False alarm P (FA) < 0.05 0.05 - 0.1 : Accepted : Conditionally accepted 0.1 : Unacceptable Probability of miss P (MISS) < 0.02 0.02 - 0.05 > 0.05 : Accepted : Conditionally accepted : Unacceptable Note: This the thumb rule . Organization & customer has To decide who much risk is Acceptable considering the Importance of the parameter Steps for interpreting MSA results MSA tools and methods - Attributes

71. MSA definition MSA Parameters MSA Tools & Methods MSA Plan & Execution Interaction points

72. P art - 4 MSA plan and execution

73. Steps to be followed to IMPLEMENT MSA Plan Prepare complete gauge list. Categorize all gauges to Major Gauge Groups which need to be covered in given time line. Refer control plan of each process to identify importance and criticality of each gauge. Select gauge as per criticality. Considering point 2 & 3 prepare MSA Plan. Plan not more than 2 MSA per day. Per f orm 100% gauges of the Gauge – list need not be covered. Perform MSA as per MSA plan. Present Analyze and Conclude study with interpretation. For MSAs out of acceptable limit, take necessary action and again perform MSA. Part with lowest tolerance to be taken for MSA. MSA plan and execution

74. Points to be confirmed before starting MSA No of Parts X No of Appraiser should be minimum 15 . Appraisers must be the users of the measurement system . Parts to be numbered from 1 to n (10) so that numbers are not visible to appraisers . Gauge should be calibrated . Parts should be clean and dent free . Measurement should be in random order . All parts should be retained after study till completion of study . Observer should have a ref copy of the MSA readings . 10 samples should represent the maximum process variation; follow Systematic sampling for selection. MSA plan and execution

75. Thank you