Recent Aspects on the Effect of Inclusion Characteristics on the Intragranular Ferrite Formation in Low Alloy Steels: A Review
Bibliographic record
Abstract
Abstract Intragranular ferrite (IGF), which nucleates from specific inclusion surfaces in low alloy steels, is the desired microstructure to improve mechanical properties of steel such as the toughness. This microstructure is especially important in the coarse grain heat affected zone (CGHAZ) of weldments. The latest review paper focusing on the role of non-metallic inclusions in the IGF formation in steels has been reported by Sarma et al. in 2009 (ISIJ int., 49(2009), 1063–1074). In recent years, large amount of papers have been presented to investigate different issues of this topic. This paper mainly highlights the frontiers of experimental and theoretical investigations on the effects of inclusion characteristics, such as the composition, size distribution and number density, on the IGF formation in low carbon low-alloyed steels, undertaken by the group of Applied Process Metallurgy, KTH Royal Institute of Technology. Related results reported in previous studies are also introduced. Also, plausible future work regarding various items of IGF formation is mentioned in each section. This work aims to give a better control of improving the steel quality during casting and in the heat affected zone (HAZ) of weldment, according to the concept of oxide metallurgy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".