Defect depth retrieval from pulsed phase thermographic data on Plexiglas and aluminum samples
Bibliographic record
Abstract
In the last few years, several quantitative inversion methods have been proposed to analyze pulsed phase thermographic data: statistical methods [1], Neural Networks [2] and wavelets [3], with a wide range of reported accuracies. In the present paper a new approach is proposed based on absolute phase contrast computations defined in a similar way as for absolute temperature contrast [4]. Phase contrast data is then used to estimate the blind frequency, i.e. the frequency at which the defect becomes visible for the 'first' time [5]. It was found an excellent agreement between defect depth z, and the corresponding blind frequencies fb. Experimental tests on Plexiglas and aluminum specimens demonstrate the potential of the technique on retrieving the depth of flat-bottomed holes. We also discuss temporal aliasing and its relationship with the phase delay images. As will be stressed, the unavoidable differences between the Continuous and the Discrete Fourier Transform of a time-dependent temperature decay signal can be effectively minimized not only by selecting a sampling frequency rate according to Shannon's Sampling Theorem (as is well-known [6]), but also by choosing an appropriate truncation window size [7].
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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) | 0.000 | 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 | 0.000 | 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".