Precise surface profilometry based on low-coherence interferometry
Bibliographic record
Abstract
Low coherence interferometry (LCI) can be used to measure the profile of industrial products, the measured sample being scanned under the LCI probe. Axial distance measurements are made using the light reflected by the surface and collected on the same optical axis used for its illumination. Therefore, when the transverse resolution is not an issue, only a narrow laser beam is required and hard-to-reach surfaces can be probed with axial accuracy in the range of 1μm. With other techniques such as triangulation the surface requires to be visible from another point-of-view and complex shapes often become inaccessible. When the LCI instrument is assembled with optical fibers, delicate instrumentation may be kept away from harsh environments and only a single optical fiber needs to get close to the measurement location. However, optical fibers are particularly sensitive to temperature and a reference is required to compensate for path length drifts. Furthermore, industrial mechanical displacement systems typically induce positioning errors much larger than the LCI instrument accuracy. One approach to circumvent these problems consists in measuring the location of another surface close to the region of interest. Such a reference surface is not always available and typically requires a second probe. We found a more practical approach by using an optical quality window located over the sample surface and moving with the sample. The laser beam from the LCI instruments travels across the window just before it reaches the samples surface. The window surface induces a first reflection (4% of the incident power) and its distance is measured by the LCI as well as the sample surface distance. Since the location of the window is fixed relative to the sample while the entire surface is scanned, out-of-plane movement of the motorized slide is compensated. High-resolution measurements are obtained by simply subtracting the window plane from the sample surface.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".