Influence of Sn on practical performances of structural steels
Bibliographic record
Abstract
The use of "recycled steel" to reduce environmental impacts is being considered by various organisations because such steel can contribute to a reduction in CO2 emissions compared with steel made of iron ores. In the case of "recycled steel", however, the concentration of impurities may increase if the steel is made of low-quality recycled materials; especially, it is difficult to remove some elements such as Cu and Sn from steels. These elements are called as "tramp elements", and the concentrations of these elements in scrap have been found to increase recently. "Recycled steel" steels are often used as structural steels (i.e., for construction purposes). Thus, recycled steel should exhibit good strength, toughness and weldability. 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. Mechanical properties, such as strength, ductility and Charpy toughness, were then examined in the directions of length and width for all the plates with different Sn concentrations, which were subjected to laboratory-casting. Furthermore, characteristics of thickness direction were also investigated to evaluate the lamellar tear susceptibility, which is a weld flaw. Microstructures and ferrite matrix hardness were measured and investigated to discuss the influence and mechanisms of Sn on the mechanical properties of steels. As a result, the yield, tensile strength, and absorbed energy at 20C were decreased, and the fracture appearance ductile to brittle transformation temperature (FATT) was increased with increasing Sn content. Toughness (FATT and absorbed energy) in the thickness direction was particularly deteriorated by Sn. It can be assumed that according to the 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".