Non-Isothermal Austenite Grain Growth Kinetics in the HAZ of a Microalloyed X-80 Linepipe Steel
Bibliographic record
Abstract
Non-isothermal austenite grain growth kinetics under the influence of several combinations of Nb, Ti and Mo containing complex precipitates has been studied in a microalloyed linepipe steel. The goal of these studies is the development of a grain growth model to predict the austenite grain size in the weld heat affected zone (HAZ). A detailed electron microscopic investigations of the as-received steel proved the presence of Ti-rich, Nb-rich and Mo-rich precipitates. Inter and intragranular precipitates of ~5-150 nm have been observed. The steel has been subjected to austenitizing heat treatments to selected peak temperatures of 950, 1150 and 1350°C at various heating rates of 10, 100 and 1000°C/s. Thermal cycles have been found to have a strong effect on the final austenite grain size. The increase in heating rate from 100 to 1000°C/s has a negligible difference in the austenite grain size irrespective of the austenitizing temperature. However, the increase in grain size has been noticed at 10°C/s heating rate for all the austenitizing temperatures. The austenite grain growth kinetics have been explained taking into account the austenite growth in the presence of precipitates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".