High-resolution non-destructive detection of grinding burns with NV diamond quantum magnetometer
Bibliographic record
Abstract
Grinding burns compromise the mechanical integrity and fatigue life of high-performance mechanical components. Traditional detection methods (nital etching, microstructural imaging, etc.) are either destructive, hazardous, or unsuitable for rapid inspections. Usual magnetic nondestructive testing (NDT) techniques offer an alternative but often fail to isolate the specific effects of grinding burns from other material properties. This study introduces a novel NDT approach utilizing Nitrogen-Vacancy (NV) centers in diamond magnetometer for the detection of grinding burns. NV centers are quantum defects in the diamond lattice that enable chemical-free, contactless quantitative vector magnetic field measurements with high spatial resolution and sensitivity and no calibration effort. NV technology was applied to detect grinding burns, successfully identifying all burns on a 16NiCrMo13 gearwheel regardless of their size or intensity. A preliminary magnetization of 20 mT was sufficient to detect even the weakest burns. Measurements over 10 × 10 mm areas were completed in minutes, with effective measurement times on the order of one second. This approach eliminates the need for hazardous chemicals and offers precise magnetic characterization, opening doors to large-scale industrial deployments.
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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 | 0.000 | 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".