Real-time approach for thermal non destructive testing
Bibliographic record
Abstract
The work presented in this paper has the goal to derive equations that permit experimental design of active non-destructive infrared testing. The approach consists of the predetermination of the required frequency for the input thermal signal given the size of a variety of possible defects in materials and the time when steady state response can be recorded. Infrared testing is known for the ability to give thermal images that are influenced by inner defects. For this reason, in the proposed approach the model used for the experimental design of non-destructive infrared active testing of materials is reduced from full three-dimensional (3D) to one-dimensional (1D). It is considered that, in some specific conditions, relevant results can be obtained before reflected thermal waves from more remote boundaries arrive back to the point under investigation. The analysis starts with the formulation and solution of the direct problem for three-dimensional (3D) heat conduction problem, followed by detailed development for the one-dimensional (1D) problem and ends with simulation results that illustrate the usefulness of the pre-determination of the frequency of the input thermal signal needed for the investigation of possible defects in materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".