Bibliographic record
Abstract
The use of fatigue data, generated using a simple periodic overload test sequence, can provide several advantages over conventionally measured fatigue data. These include more realistic and accurate fatigue life predictions for variable amplitude loading histories and shorter testing times for prototypes and fatigue test articles provided by editing non-damaging cycles from measured loading spectra. The technique involves testing uniaxial fatigue coupons using a fully reversed periodic overload of near-yield magnitude with a fixed period in an otherwise constant amplitude sequence of high stress ratio smaller cycles. The tests can be performed in either load control or strain control with the former offering the advantage of speed and the latter the advantage of greater precision and control of the tests. The net outcome of this procedure is an effective strain life curve for a given material. When used as the basis for fatigue damage calculations, this type of data can give realistic fatigue life estimates for variable amplitude loading conditions. In addition, the endurance limit exhibited by the effective strain life data, which is referred to as the intrinsic fatigue limit, provides a material-based criterion for non-damaging cycles in fatigue. This intrinsic fatigue limit can be of large magnitude in typical engineering alloys and has a practical application as a parameter for filtering non-damaging cycles from rainflow-counted fatigue service load histories. It is shown empirically that the magnitude of the intrinsic fatigue limit can be estimated from the modulus of elasticity of the material. This paper describes a procedure for measuring the effective strain life curve for a material and demonstrates how to apply the measured data to calculate fatigue life for variable amplitude loading spectra with the aid of computer algorithms. Finally, a technique for editing service load histories to remove non-damaging cycles from service load spectra is described.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".