Continuous cooling transformation behaviour and toughness of heat-affected zones in an X80 line pipe steel
Bibliographic record
Abstract
A continuous cooling transformation (CCT) diagram was developed using thermal simulation techniques to replicate the coarse-grain heat-affected zone (CGHAZ) of an X80 line pipe steel. Specimens were heated to a peak temperature of 1350 °C with a 1 s hold time, followed by cooling at several rates between 7.6 and 356 °C/s. The thermo-mechanical specimen design was compatible with sub-size instrumented Charpy impact testing, which allowed the toughness to be evaluated. Following dilatometry measurements during continuous cooling, specimens were prepared and examined using optical and scanning-electron microscopy. In addition, the initial CGHAZ microstructure (upper bainite) was reheated to a peak temperature of 850 °C for 1 s (double cycled) and cooled at three different cooling rates (10, 5 and 2 °C/s). This allowed the influence of intercritical reheating, which occurs in multi-pass welds to be investigated in terms of microstructure, tensile strength and notch toughness. It was observed that the microstructure consisted of effective grain (matrix) and untempered MA when cooling at 10 or 5 °C/s. However, when reheating was followed by a cooling rate of 2 °C/s, the microstructure consisted of a combination of effective grain and a more benign distribution of tempered MA, which provided better low temperature impact toughness.
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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 | 0.000 | 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".