A combined integral transform asymptotic expansion method for the characterization of interface flaws through pulsed infrared thermography
Bibliographic record
Abstract
This work is devoted to the non-destructive evaluation of materials using stimulated infrared thermography. The proposed approach provides simple analytical solutions to evaluate the lateral extent, and the thickness of a plane flaw in a three-dimensional (3D) heat transfer configuration. It is based on the application of a Laplace transform on the time variable t, then a double Fourier transform on the space variables x and y. Reduction of the models is obtained through an asymptotic expansion method. This mathematical formalism leads to the construction of explicit relationships that are very convenient for quantitative inversion. An experimental validation of the inversion procedures is performed on calibrated carbon-epoxy laminates. A particular attention is dedicated in the second part of this work to the experimental analysis of the impact of the definition of the experimental thermal contrast and the sane (i.e., non-defective) area on the inverted parameters. The analysis is carried out through a comparison between two classical definitions of the thermal contrast and a new method, the DAC contrast, developed recently by our team. The impact of a hyper parameter of the inversion procedures (i.e., the Laplace variable) is also analyzed. Both analyses are illustrated through an experiment aiming to estimate the depths of some calibrated flaws in a CFRP laminate.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".