The new ball-rolling mill of EVRAZ NTMK – new possibilities for customers
Bibliographic record
Abstract
In view of increase of ferrous and nonferrous ores mining, the need for grinding balls, used for the ores grinding in the process of concentration, is also increasing. Description of technical solutions, realized at accomplishment of an investment project at JSC EVRAZ NTMK, presented. The investment project on the technical modification of the rail and beam shop included construction of the new ball-rolling mil further to production of steel grinding balls. The new ball-rolling mill, which was put into operation in 2018, has a possibility to produce ball of diameter from 60 up to 120 mm with hardness up to 5th group. The mill is equipped by an automation system of the process and balls stocking control. The ball production section comprises a walking beams heating furnace with automated heating modes to ensure energy-efficient operation; automated hot rolling mill for production grinding balls of 60 ‒ 120 mm diameter; balls thermal treatment line, including temperature leveling facility, quenching machine and tempering furnace. The system of the process automation enables to trace on-line all the technological parameters for production quality products, parameters of the environment to increase the energy efficiency of the section, and control system to create safety working area at the section. Within a year after the ball-rolling mill commissioning, the whole nomenclature series of balls was mastered and the passport productivity at each profile was reached. The maximum mill productivity obtained was 22 t/h. The JSC EVRAZ NTMK mill of grinding balls of 60 ‒ 120 mm diameter production meets all the modern market requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.012 |
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".