Subhodeep Deb, profile picture
Uploaded bySubhodeep Deb
PPT, PDF1,144 views

Measurement System Analysis - Module 2

The document outlines the methodology for conducting Measurement System Analysis (MSA), including definitions, parameters, tools, and execution steps. It details various studies such as bias, linearity, stability, and methods for both variable and attribute measurements, emphasizing the importance of repeatability and reproducibility. Additionally, it provides guidelines for interpreting results and decision-making based on the analysis outcomes.

Embed presentation

Downloaded 46 times
1Subhodeep Krishna Deb Subhodeep Krishna Deb Module 2
2Subhodeep Krishna Deb MSA definition MSA Parameters MSA Tools & Methods MSA Plan & Execution Interaction points
3Subhodeep Krishna Deb 3 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
4Subhodeep Krishna Deb 4 Steps of performing MSA Select 3 appraisers. Arrange 10 samples. 1st appraiser to take readings of all 10 samples. 2nd appraiser then takes reading of 10 samples. 3rd 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
5Subhodeep Krishna Deb 5 Steps for calculating MSA results USL: LSL: Tol: 0.0000 1 2 3 4 5 6 7 8 9 10 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xa= #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Ra= 0.0000 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xb = #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Rb= 0.0000 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xc= #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Rc= 0.0000 Specification: Gauge ID: Date: APPRAISER TRIAL PART AVERAGE Appraiser 1 Appraiser 2 Appraiser 3 Xp= #DIV/0! Rp= #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Average of Range = (Ra + Rb + Rc / No. of Appraiser) R = Part Average #DIV/0! 0.0000 Range of Average = (Max. X - Min. X) XDIFF = #DIV/0! Upper Control Limit for Range Chart UCLr = R x D4 0.0000
6Subhodeep Krishna Deb From Data Sheet R = 0.0083 Xdiff = 0.0024 Rp= 0.1434 Repeatability (Euipment Variation) % Repeatability EV = R x K1 Trials K1 % EV = 100 [ EV / TV ] = 10.8434 = 0.0049 2.0000 0.8862 3.0000 0.5908 % EV = 100x[EV/(TOL/6)] = 7.3850 K1 = 1/d2* (at m = no of trials, g = no of part x no of appraisers = greater than 15) Reproducibility (Appraiser Variation) % Reproducibility AV = [( Xdiff x K2 )2 - (EV 2 / nr)] % AV = 100 [ AV / TV] = 1.9303 = 0.0009 Change value of K2 as per % AV = 100x[AV/(TOL/6)] = 1.315 your case n = No of Parts = 10.0000 2.0000 3.0000 r = No of Trials = 3.0000 0.7071 0.5231 K2 = 1/d2* (at m = no of appraisers, g = 1) Repeatability & Reproducibility (R & R) % Repeatability & Reproducibility (R & R) GR & R = (EV2 + AV2 ) % R & R = 100 [ R&R / TV ] = 11.0139 = 0.0050 % R & R = 100 x GR&R/(TOL/6) = 7.50 Part Variation ( PV ) % Part Variation ( PV ) Parts K3 PV = Rp x K3 2.0000 0.7071 % PV = 100 [ PV / TV ] = 99.3916 3.0000 0.5231 = 0.0451 4.0000 0.4467 % PV = 100 x PV/(TOL/6) = 67.69 5.0000 0.4030 Total Variation ( TV ) 6.0000 0.3742 % TV = 100 x TV/(TOL/6) = 68.11 7.0000 0.3534 TV = GRR2 + PV2 8.0000 0.3375 Number of distinct Data Categories 9.0000 0.3249 ndc = 1.41 [ PV / R&R ] = 0.0454 10.0000 0.3146 = 12.7241 Data Categories Appraisers K2 Steps for calculating MSA results
7Subhodeep Krishna Deb 1. More than 50% points in Average graph should be outside control limits. 2. Average graph reflects the Measurement Capability of the Measurement System. 3. Range graph reflects the Measurement Consistency of the Measurement System. 4. All points in range graph should remain within control limit. 5. NDC reflects no of discrete categories permissible in Measurement System. Steps for interpreting MSA results
8Subhodeep Krishna Deb 1. AVERAGE CHART 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 Xp= X dbar D Rakshit 40.850 40.837 40.840 40.850 40.850 40.867 40.870 40.860 40.850 40.880 40.855 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 A2 = 1.023 for n=3, 1.88 for n=2 (n=number of appraisers) R bar Range of average 0.010 0.003 0.007 0.003 0.000 0.007 0.000 0.000 0.010 0.010 0.005 40.810 40.820 40.830 40.840 40.850 40.860 40.870 40.880 40.890 1 2 3 4 5 6 7 8 9 10 Average Chart D Sarkar S K Sinha D Rakshit UCL LCL Steps for interpreting MSA results
9Subhodeep Krishna Deb 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.
10Subhodeep Krishna Deb 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 0.000 0.002 0.004 0.006 0.008 0.010 0.012 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)
11Subhodeep Krishna Deb 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 GRR – Range Chart Interpretations 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
12Subhodeep Krishna Deb 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
13Subhodeep Krishna Deb 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
14Subhodeep Krishna Deb 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.
15Subhodeep Krishna Deb Relation of R & R with Cp, Cpk Actual Cp/Cpk is always more than Observed Cp/Cpk
16Subhodeep Krishna Deb Data Qualitative Quantitative Attribute Variable 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
17Subhodeep Krishna Deb 17 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
18Subhodeep Krishna Deb 18 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.
19Subhodeep Krishna Deb LSL USL I II II IIII I. Bad product is always rejected II. Gray area, Some time good is called bad and bad is called good III. Good product is always accepted Reducing variation is goal of SPC Reducing variation is goal of MSA • Signal Detection is used for both Repeatability & Reproducibility Steps for calculating MSA results
20Subhodeep Krishna Deb 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: 2. Arrange the reference value & codes in descending orders 3. Calculate top width d1 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) 4. Calculate top width d2 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) 5. Calculate average width AW = d1+d2/2 6. Calculate % R & R = (AW/Tolerance)*100 + When the row has only 1 - When the row has only 0 x When the row has both 1 and 0
21Subhodeep Krishna Deb Reference value Varies from : 0.400 to 0.600 + When the row has only 1 - When the row has only 0 X When the row has both 1 & 0 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 0 Right Decision, Effective MS1 called 1 0 called 1 Miss Alarm, Consumer’s risk, b 1 called 0 False Alarm, Producer’s risk, a < 10 % Acceptable < 30 % Conditionally Acceptable > 30 % Needs improvement Steps for interpreting MSA results
22Subhodeep Krishna Deb 22 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
23Subhodeep Krishna Deb 23 Steps of performing MSA 1. Take 50 parts randomly from the process covering the entire variation (ensure at least 20% of the parts are defective 2. Get measurements on the parts by the operators as 1 (for OK) and 0 (for NOT OK) decisions 3. 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. 4. Get these parts measured / decided by a MASTER (an experienced) person for the results to be used as REFERENCE 5. Compare each trial of each inspector with the another inspector for their decision 6. Complete the cross tabulation table, shown in next slide 7. Calculate Kappa for - A vs B, A vs C, B vs C - A vs Ref, B vs Ref, C vs Ref
24Subhodeep Krishna Deb 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
25Subhodeep Krishna Deb 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
26Subhodeep Krishna Deb Total : where A-x = 1 and B-x = 1 = 34+32+31 = 97 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) Steps for calculating MSA results
27Subhodeep Krishna Deb Count & Expected Count A*B Cross Tabulation B Total 0 1 A 0 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 = 103 Row Total = 100 Grand Total = 150 Hence, Expected count = (103 x 100)/150= 68.7 Steps for calculating MSA results
28Subhodeep Krishna Deb A*B Cross Tabulation B Total 0 1 A 0 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 po = sum of observed proportions in the diagonal cells (left to right direction) pe = sum of expected proportions in the diagonal cells (left to right direction) Steps for calculating MSA results
31Subhodeep Krishna Deb 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
32Subhodeep Krishna Deb 32 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
33Subhodeep Krishna Deb • 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 Limitations:
34Subhodeep Krishna Deb 1. Collect min 20 samples covering good, bad (include marginal one which is part of process) 2. Decide the reference value-It should be inline with internal / external customer requirement. Team should come with common consensus on reference value 3. Identify the parts with numbers 4. Ask a operator who is regularly checking these product to separate good and bad parts 5. Record his decision about every part as good and bad 6. Repeat step 4 and 5 with 2-3 operators for at 2-3 times 7. Calculate Effectiveness of inspection, miss & false alarm 8. Decide whether measurement system is accepted Steps of performing MSA
35Subhodeep Krishna Deb Srl APPRAISER:A APPRAISER:B APPRAISER:C Trials 1 Trials 2 Trials 3 Trails 1 Trails 2 Trails 3 Trials 1 Trials 2 Trials 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 Decision Miss Alarm B False Alarm Correct Decision Steps for calculating MSA results
36Subhodeep Krishna Deb Ref G B A G 33 2 B 3 22 Number of samples (N)=20 Number of Good samples (NG)=12 Number of trials (R)=3 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 B G 35 1 B 1 23 Ref G B C G 34 5 B 2 19 Miss Rate (Consumer’s Risk) Max 2 % False Alarm rate (Producer’s Risk) Max 5 % Steps for calculating MSA results
37Subhodeep Krishna Deb Effectiveness (E) > 0.9 : accepted 0.8-0.9 : conditionally accepted < 0.8 : unacceptable Probability of False alarm P (FA) < 0.05 : Accepted 0.05 - 0.1 : Conditionally accepted > 0.1 : Unacceptable Probability of miss P (MISS) < 0.02 : Accepted 0.02 - 0.05 : Conditionally accepted > 0.05 : 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
38Subhodeep Krishna Deb MSA definition MSA Parameters MSA Tools & Methods MSA Plan & Execution Interaction points
39Subhodeep Krishna Deb 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. Perform •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.
40Subhodeep Krishna Deb Points to be confirmed before starting MSA 1. No of Parts X No of Appraiser should be minimum 15 2. Appraisers must be the users of the measurement system 3. Parts to be numbered from 1 to n (10) so that numbers are not visible to appraisers 4. Gauge should be calibrated 5. Parts should be clean and dent free 6. Measurement should be in random order 7. All parts should be retained after study till completion of study 8. Observer should have a ref copy of the MSA readings 9. 10 samples should represent the maximum process variation; follow Systematic sampling for selection.
41Subhodeep Krishna Deb For related assistance & service: subhodeepkrishnadeb@gmail.com

More Related Content

PPSX
Measurement System Analysis
byQualimation Technologies
 
PPT
Measurement System Analysis - Module 1
bySubhodeep Deb
 
PDF
10. measurement system analysis (msa)
byHakeem-Ur- Rehman
 
PPT
MSA
byJitesh Gaurav
 
PPTX
Msa training
byAlokSharma348
 
PPT
02training material for msa
by營松 林
 
PPTX
Attribute MSA
bydishashah4993
 
PPT
Msa presentation
byDr. Bikram Jit Singh
 
Measurement System Analysis
byQualimation Technologies
 
Measurement System Analysis - Module 1
bySubhodeep Deb
 
10. measurement system analysis (msa)
byHakeem-Ur- Rehman
 
MSA
byJitesh Gaurav
 
Msa training
byAlokSharma348
 
02training material for msa
by營松 林
 
Attribute MSA
bydishashah4993
 
Msa presentation
byDr. Bikram Jit Singh
 

What's hot

PPT
MSA (GR&R)
byMANISH CHOUDHARY
 
PPTX
MSA presentation
bysanjay deo
 
PPT
Measurement systems analysis v1.1
byAlexander Polyakov
 
PDF
Understanding Gage R&R Analysis
byInstron
 
PDF
Attribute MSA presentation
byPRASHANT KSHIRSAGAR
 
PPT
Measurement system analysis
byTina Arora
 
PPT
Msa training
byJitesh Gaurav
 
PPTX
Attribute MSA
bydishashah4993
 
PPTX
Measurement System Analysis (MSA)
byRam Kumar
 
PPT
Msa 5 day
byJitesh Gaurav
 
PPTX
Measurement System Analysis
byRonald Shewchuk
 
PPT
Measurement system analysis
byPPT4U
 
PDF
Gr&r studies
byshilpi020
 
PPTX
Msa attribute study
byVipul Wadhwa
 
PPTX
02trainingmaterialformsa (1) 111030062223-phpapp02
byJunelly Grace Catalan-Tecson
 
PPTX
Measurement Systems Analysis - Variable Gage R&R Study Metrics, Applications ...
byGabor Szabo, CQE
 
PDF
NG BB 25 Measurement System Analysis - Attribute
byLeanleaders.org
 
PPTX
Statistical Process Control
byMarwa Abo-Amra
 
PPT
R&R Gage Analysis
byTripticon
 
PDF
Measuremen Systems Analysis Training Module
byFrank-G. Adler
 
MSA (GR&R)
byMANISH CHOUDHARY
 
MSA presentation
bysanjay deo
 
Measurement systems analysis v1.1
byAlexander Polyakov
 
Understanding Gage R&R Analysis
byInstron
 
Attribute MSA presentation
byPRASHANT KSHIRSAGAR
 
Measurement system analysis
byTina Arora
 
Msa training
byJitesh Gaurav
 
Attribute MSA
bydishashah4993
 
Measurement System Analysis (MSA)
byRam Kumar
 
Msa 5 day
byJitesh Gaurav
 
Measurement System Analysis
byRonald Shewchuk
 
Measurement system analysis
byPPT4U
 
Gr&r studies
byshilpi020
 
Msa attribute study
byVipul Wadhwa
 
02trainingmaterialformsa (1) 111030062223-phpapp02
byJunelly Grace Catalan-Tecson
 
Measurement Systems Analysis - Variable Gage R&R Study Metrics, Applications ...
byGabor Szabo, CQE
 
NG BB 25 Measurement System Analysis - Attribute
byLeanleaders.org
 
Statistical Process Control
byMarwa Abo-Amra
 
R&R Gage Analysis
byTripticon
 
Measuremen Systems Analysis Training Module
byFrank-G. Adler
 

Similar to Measurement System Analysis - Module 2

PPT
MSA STUDY training presentation for manufacturing
byabhishek558363
 
PPTX
Measurement System Analysis - MSA.pptx
byMohitpathak59
 
PPTX
Measurement System Analysis - MSA.pptx
byMohitpathak59
 
PPTX
MSA R&R for training in manufacturing industry
byabhishek558363
 
PPT
Msa la
byPaul Robere
 
PDF
Measurement System Analysis Case Study for Learning
byankity8102000
 
PPT
MSA Minitab temas estadisticos academicos.ppt
byeuserodriguez
 
PPT
Measurement system analysis Presentation.ppt
byjawadullah25
 
PDF
Measurement systems analysis and a study of anova method
byeSAT Journals
 
PDF
Msa rr
byChai Wei
 
PPTX
MSA – Gage R&R Test
byMatt Hansen
 
PPS
R&R Analysis Using SEDana
byamegens
 
PPTX
Measurement System Analysis - A Detailed Study
byIlinaRoy1
 
PPTX
Measurement System Analysis - A new Approach
byIlinaRoy1
 
PPT
Six sigma-in-measurement-systems-evaluating-the-hidden-factory (2)
byBibhuti Prasad Nanda
 
PDF
Measure System analysis and Gauge repeatability and reproducibility.
byErVipulAnand
 
PPT
23 Measurement Systems Analysis for detail study.ppt
byRajKesav
 
PPTX
GP_Training_Introduction-to-MSA__RevAF.pptx
byssuserbcf0cd
 
PDF
MSA 4th edition
byduongle0
 
PPTX
Training on the topic MSA as per new RevAF.pptx
bySantoshKale31
 
MSA STUDY training presentation for manufacturing
byabhishek558363
 
Measurement System Analysis - MSA.pptx
byMohitpathak59
 
Measurement System Analysis - MSA.pptx
byMohitpathak59
 
MSA R&R for training in manufacturing industry
byabhishek558363
 
Msa la
byPaul Robere
 
Measurement System Analysis Case Study for Learning
byankity8102000
 
MSA Minitab temas estadisticos academicos.ppt
byeuserodriguez
 
Measurement system analysis Presentation.ppt
byjawadullah25
 
Measurement systems analysis and a study of anova method
byeSAT Journals
 
Msa rr
byChai Wei
 
MSA – Gage R&R Test
byMatt Hansen
 
R&R Analysis Using SEDana
byamegens
 
Measurement System Analysis - A Detailed Study
byIlinaRoy1
 
Measurement System Analysis - A new Approach
byIlinaRoy1
 
Six sigma-in-measurement-systems-evaluating-the-hidden-factory (2)
byBibhuti Prasad Nanda
 
Measure System analysis and Gauge repeatability and reproducibility.
byErVipulAnand
 
23 Measurement Systems Analysis for detail study.ppt
byRajKesav
 
GP_Training_Introduction-to-MSA__RevAF.pptx
byssuserbcf0cd
 
MSA 4th edition
byduongle0
 
Training on the topic MSA as per new RevAF.pptx
bySantoshKale31
 

Recently uploaded

PDF
PROBLEM SLOVING AND PYTHON PROGRAMMING UNIT 2.pdf
byA R SIVANESH M.E., QIP-PG (Cyber Security)., (Ph.D)
 
PPTX
The Half-Life of Preventive Maintenance: Why PM Compliance Fails & How to Res...
byMaintWiz Technologies Private Limited
 
PPTX
Explore how AI-powered maintenance checklists transform asset reliability and...
byMaintWiz Technologies Private Limited
 
PPTX
Using Bangladesh studies in cse why matter ppp.pptx
bymsprinceahmedontor40
 
PPTX
Unit-2: Network Models--OSI Reference Model, TCP/IP Reference Model
bySuvarnaSharma5
 
PPTX
Why Air Droppable Containers Are Critical for Modern Aerial Logistics
byNeometrix_Engineering_Pvt_Ltd
 
PDF
Formality - Logic Equivalence Checking - Part 2
byAmr Adel
 
PDF
Earths-Structure-and-Composition_20260127_110958_0000.pdf
byabdurasabarfaki
 
PDF
Steel - - Welded Connections.pdf By ER. Gurmeet Singh GCET JAMMU gurmeet.b.t...
byEr. Gurmeet Singh
 
PDF
FPGA Fabric and Synthesis All Parts Combined
byAmr Adel
 
PPTX
FROM MEASUREMENT TO MEANING : Metrology-Driven Battery Testing for Reliable ...
byRunjhunDutta
 
PDF
Chip Designer's Code - Linux Terminal Part 6 - Text Viewers
byAmr Adel
 
PPTX
Applications of cloud computing in education
byhatimunwala5
 
PDF
ME25C05 RE-ENGINEERING FOR INNOVATION LAB.pdf
byVinothkumarM60
 
PPTX
CHAPTER 13 - NUCLEI - CLASS XII PHYSICS 2025-26
bybhavaneeth1
 
PDF
Chip Designer's Code - Linux Terminal Part 7.1 - Text Processing
byAmr Adel
 
PDF
RVV Basic Terms and Analogy - (en/zhTW) - DannyJiang
byDanny Jiang
 
PDF
TRUSTWORTHY AI: BUILDING SAFE AND ETHICAL MACHINE LEARNING SYSTEMS
byAdri Jovin
 
PDF
5.5 Inch 4K TFT LCD Display 3840×2160 UHD Panel – LS055D1SX05(G)
bySyluxdisplay
 
PDF
(en/zhTW)All_Roads_Lead_to_IPC_DannyJiang
byDanny Jiang
 
PROBLEM SLOVING AND PYTHON PROGRAMMING UNIT 2.pdf
byA R SIVANESH M.E., QIP-PG (Cyber Security)., (Ph.D)
 
The Half-Life of Preventive Maintenance: Why PM Compliance Fails & How to Res...
byMaintWiz Technologies Private Limited
 
Explore how AI-powered maintenance checklists transform asset reliability and...
byMaintWiz Technologies Private Limited
 
Using Bangladesh studies in cse why matter ppp.pptx
bymsprinceahmedontor40
 
Unit-2: Network Models--OSI Reference Model, TCP/IP Reference Model
bySuvarnaSharma5
 
Why Air Droppable Containers Are Critical for Modern Aerial Logistics
byNeometrix_Engineering_Pvt_Ltd
 
Formality - Logic Equivalence Checking - Part 2
byAmr Adel
 
Earths-Structure-and-Composition_20260127_110958_0000.pdf
byabdurasabarfaki
 
Steel - - Welded Connections.pdf By ER. Gurmeet Singh GCET JAMMU gurmeet.b.t...
byEr. Gurmeet Singh
 
FPGA Fabric and Synthesis All Parts Combined
byAmr Adel
 
FROM MEASUREMENT TO MEANING : Metrology-Driven Battery Testing for Reliable ...
byRunjhunDutta
 
Chip Designer's Code - Linux Terminal Part 6 - Text Viewers
byAmr Adel
 
Applications of cloud computing in education
byhatimunwala5
 
ME25C05 RE-ENGINEERING FOR INNOVATION LAB.pdf
byVinothkumarM60
 
CHAPTER 13 - NUCLEI - CLASS XII PHYSICS 2025-26
bybhavaneeth1
 
Chip Designer's Code - Linux Terminal Part 7.1 - Text Processing
byAmr Adel
 
RVV Basic Terms and Analogy - (en/zhTW) - DannyJiang
byDanny Jiang
 
TRUSTWORTHY AI: BUILDING SAFE AND ETHICAL MACHINE LEARNING SYSTEMS
byAdri Jovin
 
5.5 Inch 4K TFT LCD Display 3840×2160 UHD Panel – LS055D1SX05(G)
bySyluxdisplay
 
(en/zhTW)All_Roads_Lead_to_IPC_DannyJiang
byDanny Jiang
 

Measurement System Analysis - Module 2

  • 1.
  • 2.
    2Subhodeep Krishna Deb MSAdefinition MSA Parameters MSA Tools & Methods MSA Plan & Execution Interaction points
  • 3.
    3Subhodeep Krishna Deb3 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
  • 4.
    4Subhodeep Krishna Deb4 Steps of performing MSA Select 3 appraisers. Arrange 10 samples. 1st appraiser to take readings of all 10 samples. 2nd appraiser then takes reading of 10 samples. 3rd 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
  • 5.
    5Subhodeep Krishna Deb5 Steps for calculating MSA results USL: LSL: Tol: 0.0000 1 2 3 4 5 6 7 8 9 10 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xa= #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Ra= 0.0000 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xb = #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Rb= 0.0000 1 2 3 Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Xc= #DIV/0! Range 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Rc= 0.0000 Specification: Gauge ID: Date: APPRAISER TRIAL PART AVERAGE Appraiser 1 Appraiser 2 Appraiser 3 Xp= #DIV/0! Rp= #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Average of Range = (Ra + Rb + Rc / No. of Appraiser) R = Part Average #DIV/0! 0.0000 Range of Average = (Max. X - Min. X) XDIFF = #DIV/0! Upper Control Limit for Range Chart UCLr = R x D4 0.0000
  • 6.
    6Subhodeep Krishna Deb FromData Sheet R = 0.0083 Xdiff = 0.0024 Rp= 0.1434 Repeatability (Euipment Variation) % Repeatability EV = R x K1 Trials K1 % EV = 100 [ EV / TV ] = 10.8434 = 0.0049 2.0000 0.8862 3.0000 0.5908 % EV = 100x[EV/(TOL/6)] = 7.3850 K1 = 1/d2* (at m = no of trials, g = no of part x no of appraisers = greater than 15) Reproducibility (Appraiser Variation) % Reproducibility AV = [( Xdiff x K2 )2 - (EV 2 / nr)] % AV = 100 [ AV / TV] = 1.9303 = 0.0009 Change value of K2 as per % AV = 100x[AV/(TOL/6)] = 1.315 your case n = No of Parts = 10.0000 2.0000 3.0000 r = No of Trials = 3.0000 0.7071 0.5231 K2 = 1/d2* (at m = no of appraisers, g = 1) Repeatability & Reproducibility (R & R) % Repeatability & Reproducibility (R & R) GR & R = (EV2 + AV2 ) % R & R = 100 [ R&R / TV ] = 11.0139 = 0.0050 % R & R = 100 x GR&R/(TOL/6) = 7.50 Part Variation ( PV ) % Part Variation ( PV ) Parts K3 PV = Rp x K3 2.0000 0.7071 % PV = 100 [ PV / TV ] = 99.3916 3.0000 0.5231 = 0.0451 4.0000 0.4467 % PV = 100 x PV/(TOL/6) = 67.69 5.0000 0.4030 Total Variation ( TV ) 6.0000 0.3742 % TV = 100 x TV/(TOL/6) = 68.11 7.0000 0.3534 TV = GRR2 + PV2 8.0000 0.3375 Number of distinct Data Categories 9.0000 0.3249 ndc = 1.41 [ PV / R&R ] = 0.0454 10.0000 0.3146 = 12.7241 Data Categories Appraisers K2 Steps for calculating MSA results
  • 7.
    7Subhodeep Krishna Deb 1.More than 50% points in Average graph should be outside control limits. 2. Average graph reflects the Measurement Capability of the Measurement System. 3. Range graph reflects the Measurement Consistency of the Measurement System. 4. All points in range graph should remain within control limit. 5. NDC reflects no of discrete categories permissible in Measurement System. Steps for interpreting MSA results
  • 8.
    8Subhodeep Krishna Deb 1.AVERAGE CHART 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 Xp= X dbar D Rakshit 40.850 40.837 40.840 40.850 40.850 40.867 40.870 40.860 40.850 40.880 40.855 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 A2 = 1.023 for n=3, 1.88 for n=2 (n=number of appraisers) R bar Range of average 0.010 0.003 0.007 0.003 0.000 0.007 0.000 0.000 0.010 0.010 0.005 40.810 40.820 40.830 40.840 40.850 40.860 40.870 40.880 40.890 1 2 3 4 5 6 7 8 9 10 Average Chart D Sarkar S K Sinha D Rakshit UCL LCL Steps for interpreting MSA results
  • 9.
    9Subhodeep Krishna Deb 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.
  • 10.
    10Subhodeep Krishna Deb 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 0.000 0.002 0.004 0.006 0.008 0.010 0.012 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)
  • 11.
    11Subhodeep Krishna Deb ConditionInterpretation 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 GRR – Range Chart Interpretations 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
  • 12.
    12Subhodeep Krishna Deb GRR-AcceptanceGuideline % 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
  • 13.
    13Subhodeep Krishna Deb ProductControl 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
  • 14.
    14Subhodeep Krishna Deb WhenRepeatability > 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.
  • 15.
    15Subhodeep Krishna Deb Relationof R & R with Cp, Cpk Actual Cp/Cpk is always more than Observed Cp/Cpk
  • 16.
    16Subhodeep Krishna Deb Data Qualitative Quantitative Attribute Variable MSAMethods 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
  • 17.
    17Subhodeep Krishna Deb17 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
  • 18.
    18Subhodeep Krishna Deb18 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.
  • 19.
    19Subhodeep Krishna Deb LSLUSL I II II IIII I. Bad product is always rejected II. Gray area, Some time good is called bad and bad is called good III. Good product is always accepted Reducing variation is goal of SPC Reducing variation is goal of MSA • Signal Detection is used for both Repeatability & Reproducibility Steps for calculating MSA results
  • 20.
    20Subhodeep Krishna Deb Stepsfor 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: 2. Arrange the reference value & codes in descending orders 3. Calculate top width d1 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) 4. Calculate top width d2 (distance between last part accepted by all the appraisers to the first part rejected by all (for all specifications) 5. Calculate average width AW = d1+d2/2 6. Calculate % R & R = (AW/Tolerance)*100 + When the row has only 1 - When the row has only 0 x When the row has both 1 and 0
  • 21.
    21Subhodeep Krishna Deb Referencevalue Varies from : 0.400 to 0.600 + When the row has only 1 - When the row has only 0 X When the row has both 1 & 0 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 0 Right Decision, Effective MS1 called 1 0 called 1 Miss Alarm, Consumer’s risk, b 1 called 0 False Alarm, Producer’s risk, a < 10 % Acceptable < 30 % Conditionally Acceptable > 30 % Needs improvement Steps for interpreting MSA results
  • 22.
    22Subhodeep Krishna Deb22 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
  • 23.
    23Subhodeep Krishna Deb23 Steps of performing MSA 1. Take 50 parts randomly from the process covering the entire variation (ensure at least 20% of the parts are defective 2. Get measurements on the parts by the operators as 1 (for OK) and 0 (for NOT OK) decisions 3. 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. 4. Get these parts measured / decided by a MASTER (an experienced) person for the results to be used as REFERENCE 5. Compare each trial of each inspector with the another inspector for their decision 6. Complete the cross tabulation table, shown in next slide 7. Calculate Kappa for - A vs B, A vs C, B vs C - A vs Ref, B vs Ref, C vs Ref
  • 24.
    24Subhodeep Krishna Deb Thereare 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
  • 25.
    25Subhodeep Krishna Deb Thereare 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
  • 26.
    26Subhodeep Krishna Deb Total: where A-x = 1 and B-x = 1 = 34+32+31 = 97 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) Steps for calculating MSA results
  • 27.
    27Subhodeep Krishna Deb Count& Expected Count A*B Cross Tabulation B Total 0 1 A 0 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 = 103 Row Total = 100 Grand Total = 150 Hence, Expected count = (103 x 100)/150= 68.7 Steps for calculating MSA results
  • 28.
    28Subhodeep Krishna Deb A*BCross Tabulation B Total 0 1 A 0 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 po = sum of observed proportions in the diagonal cells (left to right direction) pe = sum of expected proportions in the diagonal cells (left to right direction) Steps for calculating MSA results
  • 29.
    31Subhodeep Krishna Deb EffectivenessDecision 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
  • 30.
    32Subhodeep Krishna Deb32 For Variables 1. Bias study 2. Linearity study 3. Stability study 4. R & R (Range Method) 5. GRR (X bar - R Method) 6. ANOVA MSA Methods For Attributes • Signal Detection • Cross Tabulation (Kappa) • Visual inspection study • Gauge Performance Curve
  • 31.
    33Subhodeep Krishna Deb •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 Limitations:
  • 32.
    34Subhodeep Krishna Deb 1.Collect min 20 samples covering good, bad (include marginal one which is part of process) 2. Decide the reference value-It should be inline with internal / external customer requirement. Team should come with common consensus on reference value 3. Identify the parts with numbers 4. Ask a operator who is regularly checking these product to separate good and bad parts 5. Record his decision about every part as good and bad 6. Repeat step 4 and 5 with 2-3 operators for at 2-3 times 7. Calculate Effectiveness of inspection, miss & false alarm 8. Decide whether measurement system is accepted Steps of performing MSA
  • 33.
    35Subhodeep Krishna Deb Srl APPRAISER:AAPPRAISER:B APPRAISER:C Trials 1 Trials 2 Trials 3 Trails 1 Trails 2 Trails 3 Trials 1 Trials 2 Trials 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 Decision Miss Alarm B False Alarm Correct Decision Steps for calculating MSA results
  • 34.
    36Subhodeep Krishna Deb Ref GB A G 33 2 B 3 22 Number of samples (N)=20 Number of Good samples (NG)=12 Number of trials (R)=3 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 B G 35 1 B 1 23 Ref G B C G 34 5 B 2 19 Miss Rate (Consumer’s Risk) Max 2 % False Alarm rate (Producer’s Risk) Max 5 % Steps for calculating MSA results
  • 35.
    37Subhodeep Krishna Deb Effectiveness(E) > 0.9 : accepted 0.8-0.9 : conditionally accepted < 0.8 : unacceptable Probability of False alarm P (FA) < 0.05 : Accepted 0.05 - 0.1 : Conditionally accepted > 0.1 : Unacceptable Probability of miss P (MISS) < 0.02 : Accepted 0.02 - 0.05 : Conditionally accepted > 0.05 : 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
  • 36.
    38Subhodeep Krishna Deb MSAdefinition MSA Parameters MSA Tools & Methods MSA Plan & Execution Interaction points
  • 37.
    39Subhodeep Krishna Deb Stepsto 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. Perform •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.
  • 38.
    40Subhodeep Krishna Deb Pointsto be confirmed before starting MSA 1. No of Parts X No of Appraiser should be minimum 15 2. Appraisers must be the users of the measurement system 3. Parts to be numbered from 1 to n (10) so that numbers are not visible to appraisers 4. Gauge should be calibrated 5. Parts should be clean and dent free 6. Measurement should be in random order 7. All parts should be retained after study till completion of study 8. Observer should have a ref copy of the MSA readings 9. 10 samples should represent the maximum process variation; follow Systematic sampling for selection.
  • 39.
    41Subhodeep Krishna Deb Forrelated assistance & service: subhodeepkrishnadeb@gmail.com