Ratcheting Prediction of 1070 and 16MnR Steel Alloys Under Uniaxial Asymmetric Stress Cycles By Means of Ohno–Wang and Ahmadzadeh–Varvani Kinematic Hardening Rules
Bibliographic record
Abstract
The present study predicts ratcheting response of 1070 and 16MnR steel samples using nonlinear kinematic hardening rules of Ohno–Wang (O–W) and Ahmadzadeh–Varvani (A–V) under uniaxial stress cycles. The ratcheting values predicted based on the O–W model were noticeably influenced by the magnitude of exponents and the number of backstress components. Taking into account both material and cyclic stress level dependent coefficients, the A–V hardening rule offered a simple framework to predict ratcheting strain over loading cycles. A comparative study of these hardening rules to assess ratcheting of 1070 and 16MnR steel samples undergoing uniaxial loading conditions resulted in a close agreement of the A–V and O–W models. The choice of hardening rules in the assessment of materials ratcheting was further discussed based on the complexity of the hardening rule, number of constants/coefficients required to characterize ratcheting response, and central processing unit (CPU) time required to run the models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".