EFFECT OF MACHINING ON IMPACT TOUGHNESS VALUE OF RAIL SAMPLES DT350 FROM STEEL GRADE 76ХФ
Bibliographic record
Abstract
It is known that the value of impact toughness is sensitive to the geometric accuracy of manufacturing, the quality of the surface and concentrator of samples for impact bending. These parameters have a particularly strong influence on samples made of high-carbon materials. At present, the main regulatory document for the manufacture of samples for impact bending is GOST 9454‒78, which does not regulate the method of applying concentrators. Currently, methods of drawing, milling and electroerosion processing are used to apply the concentrator. The issue of the influence of processing methods on the magnitude of impact strength, in particular rail steel, has not been fully studied. To study the effect of electroerosion processing on impact toughness value we made specimens of P65 rail of DT350 category from 76ХФ steel of current EVRAZ ZSMK production using CNC milling machine and electroerosion processing. It has been established that the decrease in the values of impact strength of samples made using electroerosive machining was due to martensite released during local melting of the metal and non-ferrous metals (Cu and Zn) released during processing. During the operation of the electroerosion processing machine, an uneven surface was formed when the arc locally knocked out “holes”, which serve as additional stress concentrators during impact bending tests. The application of the concentrator by a milling machine leads to the formation of a deformed layer up to 80 µm deep, which is not a stress concentrator and does not reduce the impact strength of the rail steel. As part of a further study, it is planned to investigate the effect of the application of the concentrator by the pulling method on the value of the impact toughness.
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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) | 0.002 | 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".