The Effect of Mean Stress and Mean Strain on Fatigue Damage Following Overloads
Bibliographic record
Abstract
The influence of mean stress and mean strain on fatigue damage following overloads of yield stress magnitude was investigated. Strain controlled loading spectra each consisting of a periodic fully-reversed overload cycle followed by a number of constant amplitude small cycles were applied to normalized SAE 1045 steel and 2025-T351 aluminum axial specimens. The small cycle strain range was kept constant for all tests but their position within the overload hysteresis loop was varied to observe the effect of mean stress on fatigue damage. The crack opening stress and strain were calculated as a function of the number of cycles after the overload and the effective stress range was defined as the difference between the maximum stress and the greater of either the minimum stress or the crack opening stress. Fatigue life estimates were made using a linear cumulative damage summation based on the effective stress-life fatigue curves for these materials. The experimental results indicate that the mean stress of cycles following an overload has a small but consistent effect on fatigue damage that is not explained by the effective stress range or crack closure concepts alone. Nevertheless, the calculated fatigue lives were within a factor of 2.4 of the test results.
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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 | 0.003 |
| 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".