Influence of Sn on Practical Performances of Structural Steels
Bibliographic record
Abstract
Some organisations are now considering further use of "recycled steel" to reduce environmental impacts because they believe such steel may contribute to reduce CO2 emission compared with steel made of iron ores.In the case of "recycled steel" however, impurity concentration may increase if they are made of low-quality recycled materials; especially some elements such as Cu and Sn are difficult to remove from steels.These elements are called as "tramp elements" and there even is a report that concentrations of these elements in scrap are increasing recently [1]."Recycled steel" steels are often used for structural steels (i.e., for constructions) which should have high strength, toughness and weldabilities.Sn is particularly well-known as a detrimental element for structural steels.Therefore, it is important to identify the influence of Sn on mechanical properties of structural steels.5 casted steels with different Sn concentration are rolled into 75mm thick plates and water-cooled in this study, Mechanical properties, such as strength, ductility and Charpy toughness, are then examined in the directions of length and width for all the plates.Furthermore, characteristics of thickness direction are also investigated to evaluate lamellar tear susceptibility, which is a weld flaw.In addition, microstructures and ferrite matrix hardness are measured and investigated to discuss the influence and mechanisms of Sn on the mechanical properties of steels.As the results, yield and tensile strength and absorbed energy in 20℃ are decreased and fracture appearance ductile to brittle transformation temperature (FATT) is increase with the increase in Sn.Toughness (FATT and absorbed energy) in thickness direction are particularly deteriorated by Sn.It can be assumed that, according to the considerations on tensile and hardness test results, strengthening and deterioration in toughness by Sn are due to solid solution strengthening.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".