Phase Field Modelling of Microstructure Evolution in the HAZ of X80 Linepipe Steel
Bibliographic record
Abstract
The heat affected zone (HAZ) during welding experiences a very steep temperature gradient which results in significant microstructure gradients. Thus, model approaches on the length scale of the microstructure, i.e. the so-called mesoscale, are useful to accurately simulate microstructure evolution in the HAZ. In this study, a phase field model (PFM) is employed to simulate austenite grain growth and austenite decomposition in the HAZ of an X80 linepipe steel microalloyed with Nb and Ti. The interfacial mobilities and nucleation conditions are obtained by benchmarking the PFM with experimental data from austenite grain growth and continuous cooling transformation tests. An effective grain boundary mobility is introduced for austenite grain growth to implicitly account for dissolution of NbC. Subsequently, austenite decomposition into polygonal ferrite and bainite is considered. For this purpose the PFM is coupled with a carbon diffusion model. Ferrite nuclei are introduced at austenite grain boundaries and suitable interfacial mobilities are selected to reproduce experimental ferrite formation kinetics. Bainite nucleation occurs for a sufficiently high undercooling at available interface sites (i.e. austenite grain boundaries and/or austenite-ferrite interfaces). For simplicity, the formation of carbide-free bainite is considered and a suitable anisotropy approach is proposed for the austenite-bainite interface mobility. The model is then used to predict austenite grain growth and phase transformation in the HAZ.
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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 | 0.001 | 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".