Infrared thermography for CFRP inspection: computational model and experimental results
Bibliographic record
Abstract
Infrared Thermography (IRT) is a well-known Non-destructive Testing (NDT) technique. In the last decades, it has been widely applied in several fields including inspection of composite materials (CM), specially the fiber-reinforced polymer matrix ones. Consequently, it is important to develop and improve efficient NDT techniques to inspect and assess the quality of CM parts in order to warranty airworthiness and, at the same time, reduce costs of airline companies. In this paper, active IRT is used to inspect carbon fiber-reinforced polymer (CFRP) at laminate with artificial inserts (built-in sample) placed on different layers prior to the manufacture. Two optical active IRT are used. The first is pulsed thermography (PT) which is the most widely utilized IRT technique. The second is a line-scan thermography (LST) technique: a dynamic technique, which can be employed for the inspection of materials by heating a component, line-by-line, while acquiring a series of thermograms with an infrared camera. It is especially suitable for inspection of large parts as well as complex shaped parts. A computational model developed using COMSOL Multiphysics® was used in order to simulate the inspections. Sequences obtained from PT and LST were processed using principal component thermography (PCT) for comparison. Results showed that it is possible to detect insertions of different sizes at different depths using both PT and LST IRT techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".