A Study on<i>P</i>-<i>S</i>-<i>N</i>Curve for Rotating Bending Fatigue Test for Bearing Steel
Bibliographic record
Abstract
A study on the fatigue behavior of bearing steel by using rotating bending fatigue test rigs is carried out for bearing steel (JIS SUJ2 = AISI 52100) heat-treated to HRC58-62. Several P-S-N curves and fatigue life distributions, such as Weibull and log-normal, have been used for the discussion. As a result, the best fit for a life distribution of six lots each with a sample size of around 30 specimens, at stress levels from 0.94 GPa to 1.27 GPa, is obtained by the three-parameter Weibull distribution, followed by the lognormal distribution as second, and the two-parameter Weibull distribution as the third. The observation of the broken section of the test piece reveals that the initiation point of the failure is associated almost always with subsurface non-metallic inclusions. The fatigue limit could not be observed in the experimental results. It is also proposed that the relationship between the statistical life distributions of the test series and the P-S-N curve can be expressed by the same model as the life formula by introducing a rating stress such as bearing rating load in the three-parameter Weibull and log-normal distribution used.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".