Systematic Error Correction in Robotic-OCT Inspection of Hard-to-Reach Industrial Parts
Bibliographic record
Abstract
Hard-to-reach industrial parts have complex geometries, such as internal holes and deep cavities, posing challenges for accurate 3-D measurement. Robotic-arm-assisted optical coherence tomography (robotic-OCT) is a promising method for its ability to access hard-to-reach spaces with a compact probe, high resolution, and flexibility. However, probe misalignment and nonuniform rotation in robotic-OCT systems introduce systematic errors and sacrifice measurement accuracy. In this article, we report a technique for achieving precise self-alignment between the OCT probe and workpiece by utilizing the data feedback mechanism inherent in OCT scanning. We also propose a nonuniform rotational distortion correction method to rectify distortion errors. Experimental results showed that the proposed method improved point cloud completeness to 98.7%, surpassing uncorrected (77.6% ) and manual correction (96.8% ), and reduced errors by 84.1% compared to uncorrected and 13.0% compared to manual correction, while greatly enhancing efficiency over manual correction. The robotic-OCT system, with reduced errors, achieved a measurement accuracy better than 4.5$\boldsymbol {\mu }$m for internal holes with diameters from 5 to 100 mm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".