19 Validation, measurement uncertainty and sampling for inspection
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19.1 Defining acceptance
19.1.1 False accept and false reject rates
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19.1.2 The confusion matrix in industrial terms
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19.1.3 Producer and consumer risk
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19.1.4 Setting acceptance criteria from requirements
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19.2 Repeatability and reproducibility
19.2.1 Sources of variation in a vision measurement
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19.2.2 Attribute Gauge R&R: method and calculation
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19.2.3 Variable Gauge R&R by the ANOVA method
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19.2.4 Kappa statistics and inter-rater/inter-system agreement
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19.2.5 Interpreting %R&R, %tolerance and number of distinct categories
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19.3 Confidence, tolerance and statistical intervals
19.3.1 Confidence intervals for proportions and means
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19.3.2 Tolerance intervals and coverage
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19.3.3 Confidence–reliability statements (e.g. 95/95)
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19.3.4 Propagation of uncertainty through a measurement chain
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19.4 Sampling plans for inspection validation
19.4.1 Attributes versus variables sampling
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19.4.2 Binomial and hypergeometric bases for attribute plans
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19.4.3 Operating-characteristic curves, AQL and RQL/LTPD
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19.4.4 Zero-acceptance (c=0) plans and their use
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19.4.5 Confidence–reliability sample-size formulae
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19.4.6 ANSI/ASQ Z1.4, Z1.9 and ISO 2859 in practice
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19.5 Measurement-system analysis for vision
19.5.1 Bias, linearity and stability studies
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19.5.2 Uncertainty budgets and traceability
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19.5.3 Total measurement uncertainty for vision gauges
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19.6 Verification, troubleshooting and re-validation
19.6.1 A systematic troubleshooting method
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19.6.2 Isolating optical, lighting, sensor and software failures
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19.6.3 Recording and root-cause analysis
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19.6.4 Change control and re-validation triggers
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