Non-destructive evaluation of coating thickness using guided waves
Bibliographic record
Abstract
Among existing strategies for non-destructive evaluation of coating thickness, ultrasonic methods based on the measurement of the Time-of-Flight (ToF) of high frequency bulk waves propagating through the thickness of a structure are widespread. However, these methods only provide a very localized measurement of the coating thickness and the precision on the results is largely affected by the surface roughness, porosity or multi-layered nature of the host structure. Moreover, since the measurement is very local, inspection of large surfaces can be time consuming. This article presents a robust methodology for coating thickness estimation based on the generation and measurement of guided waves. Guided waves have the advantage over ultrasonic bulk waves of being less sensitive to surface roughness, and of measuring an average thickness over a wider area, thus reducing the time required to inspect large surfaces. The approach is based on an analytical multi-layer model and intercorrelation of reference and measured signals. The method is first assessed numerically for an aluminum plate, where it is demonstrated that coating thickness can be measured within a precision of 5 micrometers using the S0 mode at frequencies below 500 kHz. Then, an experimental validation is conducted and results show that coating thicknesses in the range of 10 to 200 micrometers can be estimated within a precision of 10 micrometers of the exact coating thickness on this type of structure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".