Influence of Residual Elements on Mechanical Properties of Two Carbon Steel Grades
Bibliographic record
Abstract
Experiments were carried out to study the influence of residual elements on microstructure and mechanical properties of two carbon steel grades (i.e. 0.04% C and 0.20% C). The effect of residuals on the microstructure of the carbon steels was mainly to decrease ferrite grain sizes. The effect of residuals on tensile properties was mainly to increase yield and tensile strengths and to slightly decrease ductility, which reflected a combination of solid solution hardening by residuals and grain refinement. The 40 J notch toughness transition temperature (TT) was determined by fitting Charpy absorbed energies to a hyperbolic function and by finding the temperature corresponding to 40 J in the fitted curve; a statistical analysis was performed to ensure the repeatability of TTs defined by this procedure. After the step‐cooling heat treatment to maximize segregation, the 40 J TT of a 0.04% C steel with high Mn and Si contents increased by 28K and the 40 J TT of 0.20 % C steels with the highest residual level (0.085% Sn, 0.4% Cu and 0.4% Ni) increased by 24K, indicating that the upward shift of TT is small even for high levels of residuals. Grain boundary segregation was semi‐quantitatively analysed by Auger electron spectroscopy. Small amounts of Sn segregation were observed, most notably in the low‐C grade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".