Electrochemical fatigue sensor response to Ti–6 wt% Al–4 wt% V and 4130 steel
Bibliographic record
Abstract
Some of the finer details concerning electrochemical–mechanical interactions under cyclic stressing, observed by the electrochemical fatigue sensor (EFS), are reported, with emphasis on a series of ramp-and-hold cyclic tests on samples of hardened Ti–6 wt% Al–4 wt% V and 4130 steel. These tests were designed to further our understanding of the mechanisms underlying EFS response by revealing its timing and morphology, as well as any dependence on stress range, strain rate or mean stress. The EFS is an instrument that monitors an electrochemical current (‘the EFS current’) as a means of investigating fatigue damage in metals; specimens are fatigued in a benign electrolyte, under controlled conditions, so that their fatigue lives do not differ from those obtained in air. The results show that cyclic loading produces a cyclic EFS current with extrema located at points in the loading cycle where the strain rate is highest. Localized fatigue straining and crack-associated plasticity enhance the EFS current, producing peaks that reveal information about the timing and magnitude of plastic response on a cycle-to-cycle basis. The response depends on the cyclic variables studied in a manner that is convenient for experimentation. The EFS current reflects the superposition of two basic components: one is correlated with elastic strain and is becoming better understood; the other is directly related to plasticity.
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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 | 0.001 | 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) | 0.002 | 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".