In-situ austenite grain growth measurements in an X80 line pipe steel
Bibliographic record
Abstract
The evolution of austenite grain sizes is investigated by two in-situ techniques; laser ultrasonics for metallurgy (LUMet) and high temperature confocal scanning laser microscopy (HT-LSCM). The real-time evolution of the mean grain size is monitored during the heat treatment by means of LUMet, whereas the evolution of the surface grain structure is captured from in-situ micrographs obtained by the HT-LSCM technique. The mean grain sizes obtained from LUMet agree reasonably well with those determined from HT-LSCM measurements indicating that grain growth at the sample surface is representative for grain growth in the bulk. The evolution of the grain size distribution during heat treatment is obtained by HT-LSCM measurements. As LUMet only allows to determine mean grain sizes, the microstructural information obtained from HT-LSCM measurements complements the LUMet results. In addition, austenite reconstructions from electron backscatter diffraction (EBSD) were conducted and confirmed the results of the in-situ measurements. • High temperature laser scanning confocal microscopy (HT-LSCM) can record the evolution of surface grain size distributions. • Laser ultrasonics (LUMet) probes a volume to get indirect information on a representative mean grain size in the bulk. • By HT-LSCM and LUMet it is shown that the mean grain size evolutions at the surface and in the bulk are consistent. • This result is also confirmed by EBSD reconstruction of austenite grain structures.
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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) | 0.002 | 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".