Enhanced defect identification by image fusion of infrared thermography and ultrasonic phased array inspection techniques
Bibliographic record
Abstract
Non-destructive testing applications are one of the most crucial steps in maintaining aviation activities in a profitable and timely manner. Infrared thermography (IRT) is a functional technique that uses the thermal radiation/temperature relationship on the inspected structure to ensure efficient detection, in particular when the defect is on a surface or near the surface. Ultrasonic (UT) inspection is an alternative technique that uses the propagation of ultrasound waves into the inspected material for defect detection. While IRT suffers from detectability problems with the increasing structure thickness, UT has inspection limitations on the surface or near-surface area according to applied frequency. Overcoming these limitations of individual methods with the synergistic effect of the fusion approach might provide more precise and apparent marks for defect detection. In this study, decision-level fusion has been applied using the maximum fusion rule to combine unimodal inspection data and compare. Impact-defected Carbon Fiber Reinforced Polymer (CFRP) composite structures have been chosen to represent aerospace structures. The results show the proposed fusion approach is promising in terms of identifying defect location, size and depth to inform further stages such as repair.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".