An Integrated Model to Predict Microstructure and Mechanical Properties in the Heat Affected Zone for X80 Linepipe
Bibliographic record
Abstract
There is a complex interplay between welding procedures and the steel chemistry which determines the final engineering performance of the heat affected zone in a large diameter girth weld. This work uses a combination of experimentally determined thermal histories from laboratory scale single and dual torch multi-pass gas metal arc welding (GMAW) with phenomenological models to predict microstructure and mechanical properties in the heat affected zone (HAZ) of an X80 steel. The integrated model consists of sub-models for austenite grain growth, dissolution of Nb based precipitates and austenite decomposition. These models have been calibrated with detailed experimental studies using a Gleeble 3500 thermomechanical simulator. The models are fully integrated so that the austenite grain size and the Nb solid solution level are used as inputs into the austenite decomposition model where these two factors strongly affect the final microstructure. The decomposition model includes ferrite and bainite models with suitable criteria for transition from one model to the other and a simple first order empirical relation to predict the final fraction of martensite/retained austenite (MA). The integrated model has been applied to a variety of thermal scenarios which are derived from experimental measurements of thermal histories including dual torch conditions where, for example, the Nb solid solution level has to be tracked through both thermal excursions into austenite. Using the integrated model, microstructure maps of the HAZ can be generated for the different welding scenarios.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".