Improved Measurement-System Assessment for Processes with 100% Inspection
Bibliographic record
Abstract
We consider the assessment of an automated continuous measurement system used for 100% inspection in a high- volume manufacturing process. Because of the automation, we assume that there are no operator effects. If the system stores the measured values, we effectively know the current process mean and standard deviation. Because of the high volume, we have parts available with values spread across the whole distribution.The standard plan for measurement-system assessment is to select k parts at random from the process and measure each of the selected parts n times. We then estimate the repeatability of the system using ANOVA. We propose two improvements. First, we demonstrate the substantial value of using the known process characteristics in the analysis. Second, we describe an alternative sampling plan where we deliberately select parts with extreme values from the population of measured parts to remeasure. We call this selection leveraging. We discuss the analysis of the leveraged plan and show that it is more efficient than the standard plan. We also discuss the planning and implementation of a leveraged assessment study and some associated issues and extensions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".