Optical and Infrared Vision Non-Destructive Techniques: Integration as a means for the Defects Detection on Impacted Composite Materials
Bibliographic record
Abstract
Infrared (IR) vision has evolved in recent years from being an emerging nondestructive testing (NDT) technique to a viable approach for both aerospace manufacturing and in-service inspections.In this paper, infrared vision was applied in different spectral bands for the inspection of impacted composite materials: (1) near and short-wave infrared reflectography and transmittography, and (2) mid-wave active infrared thermography.Furthermore, optical methods, namely digital speckle photography (DSP) and holographic interferometry (HI), were used as well to highlight the damage due to the impact on the samples.In fact, experiments were carried out on two impacted panels made of aramid-phenolic composite.Some techniques provided more straightforward detection capabilities than others for different defect types.Firstly, short-wave infrared reflectography presented a good indication about the degree of the damaged area at the surface whilst near infrared transmittography provided information about the internal damage and fibre distribution.Secondly, when using mid-wave infrared thermography, advanced signal processing techniques such as principal component thermography (PCT), pulsed phase thermography (PPT), and high order statistics (HOS), were employed in order to improve surface and sub-surface damage detection on pulsed thermography (PT) sequences with good results.Finally holographic interferometry was very useful for crack detection providing complementary information to transmittography and thermography.These observations lead us to the conclusion that, when combined, these techniques could provide a robust and reliable integrated inspection system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".