Low-coherence interferometry – an advanced technique for optical metrology in industry
Bibliographic record
Abstract
Low-coherence interferometry (LCI) is an optical technique that may be used for industrial surface metrology with accuracy in the micron range. An instrument made with optical fibres is rugged enough to be used in industrial environments and the fibre-linked optical probe may be miniaturised for accessing tight locations. Among industrial applications developed at IMI, several cases for which LCI has been particularly useful, such as an elongational rheometer (RME), characterisation of wear damages on coating and laser-induced breakdown spectroscopy (LIBS), will be presented. The RME is an instrument in which a polymer sample is stretched in a controlled temperature furnace at up to 350°C. LCI has been used for monitoring the thickness of the samples. Wear damage is quantified by the volume loss after a wear test. It requires a high depth resolution (axis perpendicular to the surface) but a relatively coarse transverse resolution. The LIBS is a technique that has been used for analysing the chemical composition of materials as a function of depth. LCI has been integrated to a LIBS instrument for measuring accurately the crater depth between each laser shot.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".