NDT characterisation of carbon-fibre and glass-fibre composites using non-invasive imaging techniques
Bibliographic record
Abstract
The prerequisite for more competent and cost effective transport has led to the evolution of innovative testing and evaluation procedures.Non-destructive testing and evaluation (NDT & E) techniques for assessing the integrity of composite structures are essential to both reduce manufacturing costs and out of service time of transport means due to maintenance.Smart methods for assessing the integrity of a composite structure are essential to both reduce manufacturing costs and out of service time of the structure due to maintenance.Nowadays, thermal non-destructive testing (NDT) is commonly used for assessing composites.This research work evaluates the potential of various infrared thermography (IRT) approaches for assessing different types of fabricated defects (i.e.impact damage, inclusions for delaminations, etc) on Glass Fibre Reinforced Polymer (GFRP) and Carbon Fibre Reinforced Polymer (CFRP) plates.Measurements were performed using LWIR and three active approaches: a) pulsed thermography using the flash method (xenon flash lamps), b) transient themography using IR-heating pulse, and c) thermographic inspection for cooled sample by freezing in -20 °C and then use monitoring.Furthermore, integrated flash thermography by employing a MWIR system was also used.Finally, NIR imaging was also utilised for the inspection of the GFRP and CFRP plates.
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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) | 0.001 | 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".