Algorithmic iterative sampling in coordinate metrology plan for coordinate metrology using dynamic uncertainty analysis
Bibliographic record
Abstract
Coordinate metrology is inherently subject to a source of uncertainty due to an attempt to inspect an unknown surface based on a limited number of discrete observations called sampling points. The computation tasks required for this evaluation need to be designed and conducted to minimize the uncertainty factors during the inspection process. This work presents a novel sampling planning approach based on a probabilistic framework to estimate the uncertainty in reconstruction of the measured surface. The goal is to minimize the required number of sample points to inspect a surface flatness within an acceptable level of uncertainty. The developed methodology models the deviation from the ideal geometry is modeled as a linear combination of shape functions. Then a Probability Density Function (PDF) is created based on a prior model of the expected surface's deviation characteristics. By combining the prior probability density function and the current set of measurements, a new PDF for the reconstructed deviation is updated during the measurement process which which combines their expected values and their uncertainties. This PDF in turn can be used to estimate critical points for flatness measurement. Those critical points are in turn elected to be sampled at the next measurements. The proposed adaptive sampling is evaluated using virtual sampling of a machined surface. Results show important improvement over the commonly used random sampling approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".