Infrared thermography as a nondestructive tool for materials characterisation and assessment
Bibliographic record
Abstract
Thermographic approaches, passive and active, are widely used due to the outstanding advantages that offer in a number of applications and particularly for the assessment of materials. Nonetheless, there are limitations; depending upon the approach used, as well as on the materials thermal, optical and physical properties, proper assessment (detection and/or quantification) is feasible. In thermal non-destructive evaluation (NDE), the active approach of infrared thermography where an excitation source, such as optical flash lamps, heat lamps, hot or cold air guns, etc., is employed with the intention of inducing thermal contrasts, has several applications. The temperature differences during the transient phase appear on the material surface and so detection of subsurface defects is possible (areas of different temperatures when compared to the sound part(s) due to the different thermal diffusivity). Since the heating or cooling features of the stimulus source are identifiable (in time and amplitude) by considering the time factor quantitative assessment is also feasible. However, when a material is heated, the thermal waves penetrate the material's surface. These waves are generally of various amplitudes and frequencies and are launched into the specimen, in a transient mode (i.e. transient thermography). In this work, different applications, employing transient thermographic testing, concerning the assessment of various composite materials and components are presented. Real time NDE is presented using various transient thermography approaches, i.e. pulsed thermography (PT), pulsed phase thermography (PPT) and/or thermal modelling (TM).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".