Surface crack detection in metallic materials using sensitive microwave-based sensors
Bibliographic record
Abstract
This paper presents a highly sensitive sensor inspired by complementary split-ring resonators (CSRRs). The resonators are electrically small and designed as a near-field sensor to detect surface cracks in metallic surfaces. The sensor is etched in the ground plane of a microstrip line and fabricated using printed circuit board technology (PCB). Compared to available microwave techniques, the sensor introduced here has key advantages including high sensitivity (increasing dynamic range of the sensor), spatial resolution, design simplicity and scalability. The sensor is able numerically to detect a crack of 10 um (equivalent to λ/3400), with 200 MHz shift in the resonance frequency. For a surface crack having 200 μm width and 2 mm depth, the numerical result showed a shift in the resonance frequency of more than 2 GHz. This resonance frequency shift exceeds what can be achieved using other sensors operating in the low GHZ frequency regime by significant margin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".