Non-destructive erosive wear monitoring of multi-layer coatings using AI-enabled differential split ring resonator based system
Bibliographic record
Abstract
Unprotected surfaces where a coating has been removed due to erosive wear can catastrophically fail from corrosion, mechanical impingement, or chemical degradation, leading to major safety hazards, financial losses, and even fatalities. As a preventive measure, industries including aviation, marine and renewable energy are actively seeking solutions for the real-time and autonomous monitoring of coating health. This work presents a real-time, non-destructive inspection system for the erosive wear detection of coatings, by leveraging artificial intelligence enabled microwave differential split ring resonator sensors, integrated to a smart, embedded monitoring circuitry. The differential microwave system detects the erosion of coatings through the variations of resonant characteristics of the split ring resonators, located underneath the coating layer while compensating for the external noises. The system's response and performance are validated through erosive wear tests on single- and multi-layer polymeric coatings up to a thickness of 2.5 mm. The system is capable of distinguishing which layer is being eroded (for multi-layer coatings) and estimating the wear depth and rate through its integration with a recurrent neural network-based predictive analytics model. The synergistic combination of artificial intelligence enabled microwave resonators and a smart monitoring system further demonstrates its practicality for real-world coating erosion applications.
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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 | 0.000 | 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".