Preliminary experimental study on the electrical impedance analysis for<i>in-situ</i>monitoring of the curing of carbon/epoxy composite material for aeronautical and aerospace structures
Bibliographic record
Abstract
This paper concerns the electrical characterization of T700/M21 unidirectional composite materials using sensors developed specifically for this study. It proposes a reliable and reproducible protocol for the characterization of the material during curing. Prior to the characterization, an analysis was carried out to assess the impact of parasitic access elements (resistance of the electrode/fibre interface or of the feed wire), which was reduced to a minimum by appropriate dimensioning of the electrodes. A study of the electrical conduction in relation to the direction of the fibres made it possible to establish a suitable approach to homogenized measurement of the material. Thermo-electric coupling by self-heating was also evaluated, with a view to obtaining measurements that were not influenced by this phenomenon. Finally, the use of electrical impedance spectral analysis allowed in-situ monitoring of the curing process. The results obtained are compared with those of a rheological analysis of the same material. These results highlight the value of the proposed protocol and demonstrate that, with the aid of these sensors, complete automation of the manufacturing process of composite structures is feasible (optimization of the cure cycle by real-time automatic control).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".