Prediction of flow stress characteristics of P92 steel using a simple physically-based constitutive modelling
Bibliographic record
Abstract
This study reports the flow stress behavior of three P92 steels with different compositions. Uniaxial compression tests were conducted in the deformation temperature range of 575 °C to 650 °C and strain rate range of 0.001-0.5 s-1 using a Gleeble® 3500 thermo-mechanical simulator. A simple physically-based constitutive model was used to analyse the effects of deformation conditions (temperature and strain rate) on the metal flow stress behavior during the deformation process. The method accounts for the temperature dependence of Young’s modulus and the lattice self-diffusion coefficient of Fe in the ferrite. Constitutive equations describing the flow stress behavior of the three P92 steels were developed. From the results, the stress exponent n of 26.13(steel A), 21.61(steel B) and 27.55 (steel C) were obtained using the self-diffusion activation energy in the physically-based constitutive equation. From the results, the three steels had variation in the stress exponent values, which was attributed to differences in elemental content, such as chromium and tungsten. The developed constitutive equations were verified using statistical parameters: Pearson’s correlation coefficient (R) and average absolute relative errors (AARE). Statistical analysis showed that the three steels had the same R of 0.98, while AARE was: 1.68 (steel A), 1.72 (steel B), and 1.82 (steel C). The constitutive equations developed showed a good correlation between the experimental and predicted flow stress data. Hence, the method is applicable in describing flow stress behavior in the metalworking process in the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".