Experience in producing rails from bainitic steels 30KhG2S2АFM and 30KhG2SАFN
Bibliographic record
Abstract
The operational resistance of railway rails is mainly determined by the resistance to contact fatigue defects and wear resistance, and, in addition to the impact characteristics of rolling stock wheels, depends on the chemical composition, structure and mechanical properties of rail steel. Currently, the ways to improve the operational properties of traditional pearlitic rails by increasing the microstructure dispersion are almost exhausted. One of the solutions to increase the service life of rails may be the transition to their production from bainitic steels, characterized by higher mechanical properties, resistance to the formation of surface contact and fatigue defects and increased cold resistance. Operational tests conducted abroad in the early 2000s showed that rails made of bainitic steel do indeed have increased resistance to formation of contact fatigue defects compared to rails made of pearlitic steel, but they are subject to more intensive wear. It was concluded that the resistance of bainitic rails to head damage by surface contact and fatigue defects is a consequence of the removal of the damaged rolling surface layer as a result of wear. In 2004 – 2006, JSC EVRAZ United West Siberian Metallurgical Plant conducted research and produced an experimental batch of bainitic rails, which showed the promise of using such steel and the possibility of simultaneously providing increased wear resistance and low-temperature reliability. However, at that time, the plant did not have the full capabilities to ensure the high metallurgical quality of steel: the identified shortcomings are related to the insufficient purity of the metal for non-metallic inclusions. As part of the resumption of work on the development of bainitic rails, two experimental medium-carbon steels B1 and B2, differing in alloying schemes, were smelted, rolled onto rails of type P65 and cooled in calm air. The presented results of mechanical tests showed the positive effect of increased chromium and nickel alloying on mechanical properties and structure.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".