Etude des performances de magnétomètres à haute sensibilité pour la détection de fissures profondes
Bibliographic record
Abstract
Cet article présente les performances d'un système de contrôle non destructif (CND) par courant de Foucault mettant en oeuvre au moins un magnétomètre de type IGMRM (Improved Giant MagnetoResistance Magnetometer) et de simples inducteurs filaires. Le magnétomètre utilisé est à haute sensibilité. Il offre une grande plage de dynamique sur une large gamme de fréquence. La mise en oeuvre de ce magnétomètre a été optimisée à l'aide de simulations numériques par la méthode des éléments finis. Ces simulations ont été confrontées à des mesures expérimentales sur une cale de référence en aluminium, présentant des défauts électro-érodés. Ces résultats ont montré la robustesse et la précision du modèle numérique mis en oeuvre à l'aide du logiciel COMSOL. Par la suite, des paramètres pertinents du système (fréquence d'excitation, position, lift-off) ont été optimisés à l'aide des outils de simulation pour améliorer les performances de détection de fissures non débouchantes. In this paper, a Non Destructive Testing (NDT) Eddy Current (EC) system using at least an Improved Giant Magneto Resistance Magnetometer (IGMRM) and a single wire inducer has been evaluated. This magnetometer combines high field sensitivity performances at low frequency and high dynamic range. Numerical simulation based on the Finite Element Method has been successfully validated by comparing numerical to experimental results on a given benchmark aluminum plate. These results have shown the robustness and the accuracy of the numerical model implemented in COMSOL software. In addition, the parameters of the system like excitation frequency have been optimized by simulation. More, the performances of the detection have been analyzed with simulation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".