A Novel Linear Polarization Resistance Corrosion Sensing Methodology for Aircraft Structure
Bibliographic record
Abstract
A direct method of measuring corrosion on a structure using a micro-linear polarization resistance (μLPR) sensor is presented. The new three-electrode μLPR sensor design presented in this paper improves on existing LPR sensor technology by using the structure as part of the sensor system, allowing the sensor electrodes to be made from a corrosion resistant or inert metal. This is in contrast to a two- electrode μLPR sensor where the electrodes are made from the same material as the structure. A controlled experiment, conducted using an ASTM B117 salt fog, demonstrated the three-electrode μLPR sensors have a longer lifetime and better performance when compared to the two-electrode μLPR sensors. Following this evaluation, a controlled experiment using the ASTM G85 Annex 5 standard was performed to evaluate the accuracy and precision of the three-electrode μLPR sensor when placed between lap joint specimens made from AA7075-T6. The corrosion computed from the μLPR sensors agreed with the coupon mass loss to within a 95% confidence interval. Following the experiment, the surface morphology of each lap joint was determined using laser microscopy and stylus-based profilometry to obtain local and global surface images of the test panels. Image processing, feature extraction, and selection tools were then employed to identify the corrosion mechanism (e.g. pitting, intergranular).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".