Effect of magnesium addition in low carbon steel part 2: toughness and microstructure of the simulated coarse-grained heat-affected zone
Bibliographic record
Abstract
A critical investigation into the role of Mg on the toughness and microstructure of coarse grain heat-affected zone (CGHAZ) in low carbon steel has been investigated. In this research, the specimens (Mg-free and Mg-added) underwent weld thermal cycle with heat input of 54, 80, and 100 kJ cm −1 at 1350°C peak temperature using a thermal simulator. The typical inclusions characteristics were characterised by means of scanning electron microscopy and equilibrium calculations. The precipitates were characterised by transmission electron microscopy and energy-dispersive spectroscopy. It is revealed that the occurrence of Mg in steel mostly exists in the form of Mg-Al-O oxide inclusions, but a few in the form of solid solution state and (Nb,Ti)(C,N)+MgO precipitates when the concentration of Mg is 0.0026%. The improvement of CGHAZ toughness is obtained when the heat input is 80 and 100 kJ cm −1 . The possible reasons about the effects of Mg on the toughness of CGHAZ, including Mg-Al-O inclusions, precipitates, and soluble Mg, are discussed in detail.
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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".