Uniaxial Ratcheting Assessment of 304 Stainless Steel Samples Undergoing Step-Loading Conditions at Room and Elevated Temperatures
Bibliographic record
Abstract
Abstract The present study evaluates ratcheting response of 304 stainless steel samples subjected to various step-loading conditions at room and elevated temperatures using the kinematic hardening rules of Ohno–Wang (O–W), AbdelKarim–Ohno (AK–O), and Ahmadzadeh–Varvani (A–V). The hardening rules were employed along with the visco-plastic flow rule to account for the time-dependent response of 304 stainless steel samples. Ratcheting over low–high–low loading sequences consistently showed a small drop in ratcheting strain over the third loading step. This is mainly due to plastic strain accumulation over the first two loading steps preventing ratcheting strain to drop significantly with a drop in the mean stress. Moreover, dynamic recovery terms in these models were further modified through the inclusion of an exponential function developed by Kang to address the dynamic strain aging phenomenon. Low ratcheting rate and shakedown shortly after a few stress cycles within loading steps as operating temperatures varied between 400 and 600 °C were attributed to dynamic strain aging phenomenon in SS304 steel alloy. Progressive ratcheting response and their stress–strain hysteresis loops were highly influenced at various operating temperatures, stress levels, and stress rates. Coefficients in the dynamic recovery term of the A–V model controlled ratcheting progress and hysteresis loops agreeable with those of experimental data over consecutive loading steps. Choices of material constants and the number of segments defined from stress–strain curve based on the O–W and AK–O models noticeably influenced the ratcheting response of steel samples. Predicted ratcheting values by means of the A–V, O–W, and AK–O models were discussed and compared with those of the experimental data.
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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 | 0.000 |
| 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) | 0.002 | 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".