Pulsed phase thermography inversion procedure using normalized parameters to account for defect size variations
Bibliographic record
Abstract
Pulsed Phase Thermography (PPT) is rapidly evolving as a solid NDT&E technique. Acquisition is accomplished in a similar way as in classical Pulsed Thermography, thermal data is processed afterward using a transformation algorithm, e.g. the Fourier Transform (FT), providing amplitude and phase delay data. The authors have recently presented an extended review on PPT theory, as well as a new inversion technique for depth retrieval using phase. Furthermore, an automatic defect depth retrieval algorithm had also been presented. Due to the Time-Frequency Duality of the discrete FT, PPT sampling and truncation parameters should be carefully selected to produce the desired frequency response. An interactive methodology for the optimal selection of these parameters has been proposed. Nevertheless, this is not always a simple task. On one hand, there exists stored data for which sampling and truncation was performed without considering the time-frequency relationship; and on the other hand, there is not always possible to produce the desired frequency output because of equipment limitations. In this paper, two situations are considered. First, two composites plates (CFRP and GFRP), for which adequate parameters have been used. In this case, we demonstrate that depth can be directly estimated from the diffusion length equation as is done by Lock-In Thermography. Secondly, an aluminum specimen that has been incorrectly sampled is considered. In this case, we propose the normalized diffusion length μn, and the normalized diameter Dn, to account for defect size variation.
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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 | 0.001 |
| 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) | 0.003 | 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".