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gage-rr-msa-analyzer

Run and interpret measurement system analysis (AIAG MSA 4th edition, IATF 16949 7.1.5.1.1): crossed variable Gage R&R by ANOVA (variance components, operator x part interaction test with pooling at p > 0.25, %contribution, %study variation, %tolerance, ndc) and by the Average and Range worksheet method (EV, AV, PV, GRR, range-chart check), and attribute agreement analysis for go/no-go and visual inspection (within-appraiser agreement, effectiveness, miss rate, false alarm rate, Cohen's kappa vs the reference, system agreement). Applies the 10% / 30% and ndc >= 5 criteria, says whether repeatability or reproducibility dominates, and covers study design, %tolerance vs %study variation, bias, linearity, stability, destructive tests and how to improve gauges and inspectors. Use when qualifying a gauge for a control plan or PPAP, analysing Gage R&R data, or checking inspectors on visual or go/no-go inspection.

Manufacturing & Industrial Operations / Quality Management & ISO 9001v1.0.0Updated OCT 25 20262 references4 scripts

If the measurement system cannot tell good parts from bad, or one part from another, every downstream number (SPC, Cpk, inspection results) is noise. scripts/gage_rr_anova.py is the preferred variable study; scripts/gage_rr_xbar_r.py reproduces the AIAG hand worksheet and checks ranges; scripts/attribute_agreement.py qualifies inspectors on pass/fail decisions. Shared input checks live in scripts/_msa.py.

Trigger Conditions

Activate this skill when the user:

  • Must qualify a gauge for a control plan, PPAP or customer requirement
  • Has Gage R&R data to analyse, or results to interpret (%GRR, ndc, %tolerance)
  • Wants to check visual inspectors or go/no-go gauges
  • Sees SPC or capability results that do not make sense (often the gauge)

Workflow

text
1. DESIGN   -> 10 parts over the process range, 3 appraisers, 2-3 trials, blind random order
2. MEASURE  -> CSV: part, operator, trial, value
3. ANALYSE  -> python scripts/gage_rr_anova.py grr.csv --tolerance 0.018
4. CHECK    -> python scripts/gage_rr_xbar_r.py grr.csv --tolerance 0.018   (range-chart check, worksheet values)
5. ATTRIBUTE-> python scripts/attribute_agreement.py attribute.csv --bad reject
6. DECIDE   -> < 10% acceptable; 10-30% conditional; > 30% not; ndc >= 5
7. IMPROVE  -> fix the dominant component and re-run

Results

text
Source               Variance  %Contrib  %StudyVar  %Tolerance
Total Gage R&R      3.063e-07      1.0%      10.1%       18.4%
  Repeatability     2.504e-07      0.8%       9.1%       16.7%
Number of distinct categories (ndc): 13
Verdict on %Tolerance 18.4% and ndc 13: CONDITIONALLY ACCEPTABLE (depends on application, cost, customer approval)
Repeatability dominates: the gauge itself (fixture, resolution, wear, method) needs attention.
text
Appraiser     Within  Effective   Miss  False al.   Kappa  Grade
Ana              95%        98%   0.0%       3.6%    0.94  acceptable
Cleo             75%        78%  25.0%      21.4%    0.50  unacceptable

Study design, acceptance criteria, %tolerance vs %study variation, ANOVA vs Average and Range, bias, linearity, stability and attribute study design: study-design-and-criteria.md. Causes and fixes by dominant component, resolution rule, attribute inspection fixes: improving-measurement-systems.md.

Output Format

Deliver:

  1. Study design - parts, appraisers, trials, randomisation, gauge resolution.
  2. Results table - components with %contribution, %study variation, %tolerance; ndc; interaction test.
  3. Verdict against the criteria and the basis used (%tolerance for inspection, %study variation for SPC).
  4. Diagnosis - dominant component and its likely causes.
  5. Actions and the re-study plan.

Scripts

bash
python scripts/gage_rr_anova.py grr.csv --tolerance 0.018 [--k 5.15] [--alpha-interaction 0.25] [--json]
python scripts/gage_rr_xbar_r.py grr.csv --tolerance 0.018
python scripts/attribute_agreement.py attribute.csv --bad reject

Variable studies: columns part, operator, value (balanced). Attribute studies: part, appraiser, trial, result, reference. Exit codes: 0 acceptable, 1 conditional / not acceptable (or ranges out of control), 2 usage or data error.

Additional Resources

FileContent
study-design-and-criteria.mdStudy design, criteria, %tolerance vs %study variation, methods compared, bias/linearity/stability, attribute design
improving-measurement-systems.mdDiagnosing repeatability vs reproducibility, resolution rule, attribute fixes, re-study
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