Remote quantitative temperature and thickness measurements of plasma-deposited titanium nitride thin coatings on steel using a laser interferometric thermoreflectance optical thermometer
Bibliographic record
Abstract
An optical thermometer based on the principle of laser thermoreflectance has been introduced to monitor the surface temperature of thin coatings on steel parts undergoing an industrial titanium nitride (TiN) alloy deposition process. To study the feasibility of the optical thermometer, various thermo-optical parameters of TiN affected by the deposition process have been investigated; namely, the reflectance-temperature relation, the thermoreflectance coefficient, and the coating thickness dependence of thermoreflectance and of total reflectance. A theory of interferometric thermoreflectance has been introduced to model the total reflectance variations during the coating process. An inverse reflectance-temperature relation for the TiN–D2 steel substrate system has been found and a first-order Taylor series expansion used to model thermoreflectance has been shown to yield a thermoreflectance coefficient which is independent of temperature. Both results are in quantitative agreement with the Drude–Zener theory of conductors and semi-conductors. An empirical formula has been derived to effectively model the experimental thermoreflectance coefficient dependence of the TiN–D2 steel system on TiN coating thickness, in qualitative agreement with scattering mechanisms of the Boltzmann transport theory in conductors and semiconductors. The good agreement of theoretical interferometric thermoreflectance simulations with in situ measurements during a specific industrial TiN sputter-coating growth process and the independence of the thermoreflectance and thin-coating-thickness reflectance coefficients from temperature show the potential of using this nonintrusive noncontacting technique as an optical thermometer to determine surface temperatures of physically inaccessible samples undergoing industrial coating deposition processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".