Ratcheting of Steel Samples Undergoing Asymmetric Loading Cycles at Elevated Operating Temperatures: Analytical and Numerical Assessments
Bibliographic record
Abstract
The present study intends to assess the ratcheting response of SA508 and SA333 steel alloys subjected to asymmetric loading cycles at various operating temperatures of 298, 573, and 623K through a hardening framework developed by Ahmadzadeh–Varvani (A-V) and the finite element analysis structured by the Chaboche hardening model (CH) in the ANSYS software program. The dynamic recovery terms in the A-V and CH hardening framework consisted of temperature-dependent parameters and functions to address the dynamic strain aging (DSA) phenomenon at high temperatures of 573 and 623 K. The DSA phenomenon reported at elevated temperatures was attributed to the interactions of solute atoms and dislocations with a certain temperature, resulting in higher material strength and lower ratcheting deformation. The coefficients of these frameworks were analytically determined through stress–strain hysteresis loops obtained from the strain-controlled cyclic tests. The FE analysis was applied to numerically evaluate backstress evolution through use of the CH model. Two popular nonlinear brick and tetrahedron element types were examined to study the convergence of the elemental system with various numbers of elements. This ensured the independence of the simulated results from the number of elements and their convergence. The simulated ratcheting responses for brick and tetrahedron solid elements were compared to those predicted analytically by the A-V hardening rule and experimentally measured values. The predicted and simulated ratcheting data were found to be in good agreement with the measured data. The predicted and simulated ratcheting results generated using the A-V and FEA approaches showed R2 values of 0.96 and 0.85, respectively, when compared with 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 | 0.001 |
| 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".