Search-Guided Sampling to Reduce Uncertainty of Minimum Deviation Zone Estimation
Bibliographic record
Abstract
Integrating computational tasks in coordinate metrology and its effect on the inspection’s uncertainty is studied. It is shown that implementation of an integrated inspection system is crucial to reduce the uncertainty in minimum deviation zone (MDZ) estimation. An integrated inspection system based on the iterative search procedure and online MDZ estimation is presented. The search procedure uses the Parzen Windows technique to estimate the probability density function of the geometric deviations between the actual and substitute surfaces. The computed probability density function is used to recognize the critical points in the MDZ estimation and to identify portions of the surface that require further iterative measurements until the desired level of convergence is achieved. Reduction of the uncertainty in the MDZ estimation using the developed search method compared to the MDZ estimations using the traditional sampling methods is demonstrated by presenting experiments including both actual and virtual inspection data. The proposed search method can be used for assessing any geometric deviations when no prior assumptions about the fundamental form and distribution of the underlying manufacturing errors are required. The search method can be used to inspect and evaluate both primitive geometric features and complicated sculptured surfaces. Implementation of this method reduces inspection cost as well as the cost of rejecting good parts or accepting bad parts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".