Hall sensor notch detection in a through-transmission eddy current arrangement
Bibliographic record
Abstract
Detection of non-surface fatigue cracking represents a challenge for most conventional non-destructive inspection (NDI) techniques. This report discusses a custom electromagnetic solution and demonstrates its capabilities and shortcomings. The laboratory experiments used aluminium plates with fastener holes and notches. The notch length and penetration depth varied from 1 to 6 mm and from 25 to 100% through-wall thickness, respectively. This work embodies a two-fold novelty: (i) the through-transmission eddy current approach, with a stationary driving coil, attached to the far-side of the specimen, and (ii) the use of Hall effect solid-state sensors for the detection of the magnetic field generated by the eddy current flow in an aluminium plate containing crack-like discontinuities. The experimental results confirm that the notch characteristics, particularly the depth and length, significantly affect the magnetic response as sensed by the Hall-effect probe. The field amplitude increased with the notch length, as well as with its degree of penetration through the wall of the aluminium plate. A direct correlation between the notch length and the signal strength was established; however, for notches penetrating less than 75% of the wall thickness, no useful signal was detected. This outcome was assumed to be due to the shielding effect of the eddy currents flowing in the conductive material on top of the partially penetrating notch.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".