Passive Frequency Selective Surface Sensor Based Smart Coating for Real-Time Delamination Detection
Bibliographic record
Abstract
Coating delamination on metal surfaces, including ships and airplanes, affect their structural integrity and operational reliability, necessitating precise, real-time monitoring to detect early-stage damage. Current non-destructive coating inspection methods are either not real-time or require an active power supply to operate the sensors, limiting their applicability for sustainable usage. This work presents a passive frequency-selective surface (FSS)-based smart coating for the detection and quantification of coating delamination by correlating resonant frequency shifts with variations in the air gap between the coating and the metal surface. The developed smart coating consists of a 10x10 array of modified split-ring resonators, designed to resonate at$\sim 3.5 \text{GHz}$, and are attached to an acetate-based industrial coating. Experimental results demonstrate a sensing range up to 10 mm of air gap, with a total resonant frequency shift of 189 MHz and a maximum sensitivity of$\mathbf{4 2. 7 5 ~ M H z} / \mathbf{m m}$of air gap. The smart coating system demonstrates high sensitivity to coating delamination in the initial stages of detachment, enabling early detection and thus promising its application in aerospace, marine, and civil infrastructure industries for real-time structural health monitoring.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".