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MATHEMATICAL MODELING OF THE STRESS-STRAIN STATE OF THE METAL ROLLING IN BAR CALIBERS

2015· article· en· W2287630363 on OpenAlexaff
А. А. Уманский, V. N. Kadykov, Yu. A. Mart’yanov

Bibliographic record

VenueIzvestiya Ferrous Metallurgy · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsBar (unit)Stress–strain curveStress (linguistics)State (computer science)Strain (injury)CaliberMaterials scienceMechanicsEngineeringStructural engineeringMechanical engineeringPhysicsMathematicsFinite element methodAnatomyPhilosophyMeteorology

Abstract

fetched live from OpenAlex

With the use of specialized software DEFORM 3D the stress-strain state of the metal rolling in a simple form of high-quality caliber has been investigated. As applied to rolling conditions in box, rhombus and oval calibers it has been found interconnection between the stress-strain state of the metal in the volume of the deformed workpiece and the passage of the surface layers of the metal. With the use of the data a new calibration of light-section mill rolls 250-2 of JCS “Evraz ZSMK” has been developed, the implementation of which has improved the quality of the surface of the rolled section.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.225
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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