Implementation of advanced signal processing techniques on Line-Scan Thermography data
Bibliographic record
Abstract
In the last few years, composite materials have found an important niche of application in several industries, mainly because of their improved mechanical properties (higher stiffness, strength and resistance to fatigue). In this context, sandwich-composites, a special class of composite materials - are commonly used in the aerospace industry to manufacture lighter components. The increasing use of this type of materials in the aerospace sector has opened the necessity of inspection methods to evaluate its physical integrity and quality. Line Scan Thermography (LST) is one of the emerging technologies aimed to detect and evaluate subsurface defects present in the sandwiches composite structures. As a non-destructive testing and evaluation (NDT&E) technique, LST is a dynamic technique suited to inspect large and complex aerospace components. However, its performance to detect deeper and smaller defects is negatively affected due to the different sources of noise present in the collected thermal images. In this paper is studied the application of advanced signal processing techniques on LST data obtained from the inspection of a large composite component, which contains different types of internal defects located at a variety of depths. To evaluate the ability of each technique to reduce the noise, the signal-to-noise ratio (SNR) at the maximum signal contrast of each defect has been computed for further analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 0.003 |
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".