Photothermal Radiometry of Thermal Sprayed Coatings: Novel Roughness Elimination Methodology
Bibliographic record
Abstract
Laser infrared photothermal radiometry (PTR) was used to study various thermal sprayed coatings on carbon steel substrates. The thermophysical properties (thermal diffusivity and conductivity), interfacial defects (i.e. disbonding) and roughness effects were examined. The foregoing thermophysical parameters of the thermal sprayed coatings are obtained when a multi-parameter optimization algorithm is used to fit the PTR experimental results. Roughness effects impact the entire frequency spectrum. In a previous approach roughness was modeled as a discrete uniform layer on top of a homogeneous coating layer supported by a semi-infinite substrate. More recently an improved approach for roughness elimination was introduced, modeling roughness as white (Gaussian) noise in the spatial coordinate. Both methods have been compared quantitatively through theoretical fits to thermal-wave signals from various thermal spray coatings on carbon steel substrates. The PTR technique applied to the characterization of coatings has a strong potential as a method for non contact and in-situ characterization of thermal spray coatings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".