Eco‐Friendly Laminates: From the Indentation to Non‐Destructive Evaluation by Optical and Infrared Monitoring Techniques
Bibliographic record
Abstract
Abstract: In this work, the combined effect of indentation damage and of manufacturing defects of a hybrid laminate including jute hessian cloth (plain weave) and hemp fibres in an epoxy matrix has been investigated. With this aim, various non‐destructive evaluation (NDE) techniques have been employed, such as near‐infrared (NIR) reflectography, infrared thermography (IRT), holographic interferometry (HI) and digital speckle photography (DSP). In particular, two different methods of heating were applied during IRT data collection: pulse thermography and square pulse thermography (SPT). The first one using a mid‐wave infrared (IR) camera, while the second one using a long‐wave IR camera. In the same way, two different cameras working into the near‐ and short‐wave IR spectra were used, to compare different results from ∼ 0.74 to 14 μm. Data were processed applying principal component thermography (PCT), correlation and the robust second‐order blind identification (SOBI‐RO) algorithms. The latter is used for the first time to our knowledge in this work. The defects found were enhanced by image subtraction between the reflectogram and the transmittogram, distance transform and image fusion. In particular, data fusion from IRT and DPS images allowed clearly defining the extension of the indentation damage.
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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 | 0.000 | 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".