Multi-modal analysis of barely visible impact damage in carbon fibre composites through the fusion of Pulsed Thermography and Phased Array Ultrasonic Testing
Bibliographic record
Abstract
The increasing use of composite materials in modern aircraft structures has necessitated more efficient and reliable inspection techniques to ensure structural integrity and operational safety. Barely visible impact damage (BVID) poses a significant challenge in composite maintenance due to its subtle nature, requiring advanced non-destructive testing and evaluation (NDT&E) methods for accurate detection and characterisation. This study explores a multi-modal inspection approach that integrates phased array ultrasonic testing (PAUT) and pulsed thermography (PT) to enhance BVID detection in carbon fibre-reinforced polymer (CFRP) composites. By leveraging complementary fusion strategies, the proposed framework improves defect localisation beyond the limitations of individual techniques. The results demonstrate that fusion increased PAUT-detected sizes by up to 7% for thin specimens and 8% for thick ones, while PT-detected sizes improved by as much as 71% and 53%, respectively. These findings highlight the synergistic advantages of multi-modal NDT&E, showcasing its potential to provide complementary defect assessment and reduce uncertainty in damage evaluation. The results of this study contribute to the development of more sophisticated inspection methodologies, which ultimately support more efficient and reliable maintenance strategies in aviation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".