Ratcheting assessment of additively manufactured SS316 alloys at elevated temperatures within the dynamic strain aging domain
Bibliographic record
Abstract
The present study has evaluated the accumulation of plastic strain in additively manufactured (AM) stainless steel 316 samples undergoing asymmetric loading cycles at elevated temperature range where the dynamic strain aging (DSA) phenomenon occurred. The ratcheting response of AM steel samples were assessed through the use of the combined isotropic-kinematic hardening framework. The dynamic strain aging was introduced into the dynamic recovery term of the Ahmadzadeh-Varvani (A-V) kinematic hardening rule by an exponential function ψ( p, T ). This function enabled the hardening framework to evaluate the ratcheting of AM steel samples at operating temperatures within the DSA domain. The DSA temperature range for AM SS316 samples fell between 700 K to 1140 K. Within this temperature domain, the interaction between solute atoms and dislocations led to an increase in material strength and suppressed the magnitude of ratcheting over the loading cycles. The influence of DSA on the backstress evolution and the yield surfaces translated in the deviatoric stress space and their subsequent influence on the ratcheting of samples using the A-V model were further discussed. The predicted ratcheting curves at the DSA temperature domain were found in close agreement with those of measured values.
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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) | 0.001 | 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".