Infrared Thermography as a Rapid NDI Tool for Advanced Composite Materials
Bibliographic record
Abstract
Thermography has proven to be very useful as a Non-Destructive Inspection (NDI) method on composite materials. Using a variety of samples from different materials and of different geometric configurations, tests were performed to locate and quantify known defects and damage in those samples. The types of defects inspected ranged from, high and low velocity impact damage, to air pockets and foreign objects introduced inside the samples during the preparation process. The inspection results proved that such varying types of defects in composite materials could be easily and straightforwardly identified using thermography. One of the conclusions reached is that the infrared (IR) camera used to obtain the thermographic images needs to have a high frame capture rate and resolution in order to accurately detect all thermal information produced during the tests. In addition, the rate at which the sample is heated was observed to have a pronounced effect on the results. Different configurations of lighting were tried in order to increase the rate at which the samples were heated and to provide uniform heating across the surface of the samples. Uniform heating was essential to producing clear and sharp images.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".