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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol {\mu }$</tex-math></inline-formula>m for internal holes with diameters from 5 to 100 mm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".