Development of a Flow Rule Based on a Unified Plasticity Model for 13Cr-4Ni Low-Carbon Martensitic Stainless Steel Subject to Post-Weld Heat Treatment
Bibliographic record
Abstract
This study aims to develop a flow rule for evaluating the relaxation and redistribution of residual stresses during the post-weld heat treatment (PWHT) of hydroelectric runners made from low-carbon martensitic stainless steel (13Cr-4Ni composition). During the PWHT, austenite reforms in the filler metal and surrounding areas of the base metal near welded joints. The evolving inelastic strain rate with reformed austenite led to defining two distinct flow rules in the pure martensitic (α′) and austenitic (γ) phases. A linear rule of mixture was then applied to assess global effective stress based on the inelastic strain rate and current austenite fraction during the PWHT. A unified constitutive model incorporating drag stress and back stress, evolving with creep and plastic deformation mechanisms during the PWHT, described the stress–strain behavior. To validate this analysis, a third flow rule was determined in the 18% tempered austenitic microstructure, compared with the rule of mixture’s effective stress contribution from each phase on the inelastic strain rate. Isothermal constant strain rate tests in stabilized crystalline microstructures evaluated constants specific to their respective flow rules. This study demonstrates the stability of reformed austenite at elevated temperatures during slow cooling and its significant influence on the mechanical properties of 13Cr-4Ni steels. The effectiveness of estimating yield stress using the rule of mixture based on individual phase behaviors is also confirmed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".