Energy-based damage descriptions to assess fatigue life of steel samples undergoing various multiaxial loading spectra
Bibliographic record
Abstract
The present study evaluates fatigue damage of four steel alloys, mild steel, SS347, SNCM439, and SNCM630, by means of Socie, Ellyin, and Varvani-Farahani damage models. The Socie model assesses fatigue damage through product of maximum principal strain amplitude and maximum normal stress on the maximum principal strain plane. Damage description by Ellyin was developed as both elastic and plastic strain energies over loading spectrum were integrated. The elastic–plastic response of materials is evaluated through use of Garud’s constitutive plasticity model to achieve components of stress/ strain and corresponding stress–strain hysteresis loops. Based on the Varvani-Farahani model, components of stress and strain calculated from largest Mohr’s circles over peak-valley events are employed to assess fatigue damage. Overall damage was calculated on the basis of energy-based models from counted reversals over entire loading blocks and related to fatigue life. The Socie approach overpredicted lives for steel samples. Predicted life data for mild steel and SS347 samples fell below the midline based on the Ellyin’s model. Both Ellyin and Varvani-Farahani models showed a good agreement of predicted lives for steel samples within factors ±3 as compared with experimental data. The choice of damage assessment was highly related to consistency of damage descriptions to crack formation and early growth, stress/strain components, material properties, and loading spectrum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".