Explicit stress-strain and temperature relations for stainless steel in tension and compression
Bibliographic record
Abstract
In this paper a new stress formulation for stainless steel alloys which expresses the stress as an explicit function of strain and temperature is presented. The proposed formulation is based on approximate closed form inversion of existing two-stage stress-strain relations obtained from a modified version of the Ramberg-Osgood equation. The inversion which is obtained by making a modified power law assumption on the fractional deviation of the actual stress-strain curve from an idealized linear elastic behaviour is applicable both to tensile and compressive stresses. While previous studies use temperature dependent material properties to account for the effect of temperature on the stress-strain relation, this paper proposes explicit formulation based on the material properties at normal temperatures. The effect of temperature is introduced by modifying the form of the stress-strain relation which includes a factor with an appropriate temperature dependent function. The validity of the temperature-dependent expression is tested over a wide range of material parameters and a wide range of temperatures. It is demonstrated that the proposed expression is both qualitatively and quantitatively in excellent agreement with the fully iterated numerical solution of the full-range temperature-dependent stress-strain relation at moderate and high temperatures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".