A methodology of roll pass design calculation for rails rolling using regression equations
Bibliographic record
Abstract
In process of shaped profiles experimental rolling, it is often necessary to make adjustments to new roll pass design to ensure their physical filling, control between gaps in the values of real broadenings and reductions. In order to reduce number of possible adjustments in roll pass design process and mastering new profiles, the task of developing a model for calculating roll passes becomes actual. A model for calculating roll pass design for rolling railway rails presented. This model based on roll pass design for rolling R65 rails. The regression equations of deformation logarithmic degree were derived using example of two pre-finishing and finishing roll passes, which were used to calculate UIC60E1 rail roll pass design. A high degree of convergence of the geometry of the calculated and operating roll pass has been established. The maximum deviations from geometry of existing roll pass and calculated ones according to proposed method did not exceed 1-2 mm on each of them. It was shown that the developed model calculates exact roll pass geometry, which can be loaded into the program of a processing machine. The proposed calculation methodology can be used at roll pass design of shaped profiles of the same type of different profile sizes of the same mill, resulting in significant reduction of roll pass designed development time and number of adjustments.
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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 | 0.003 | 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 | 0.000 | 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".