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Distribution of temperature fields and specific reductions at grinding balls rolling

2019· article· en· W2964353685 on OpenAlexaff
V. Yu. Rubtsov, О. И. Шевченко, V. V. Kurochkin, A. S. Oparin

Bibliographic record

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsFlangeGrindingBall (mathematics)Materials scienceEquatorBall millMechanicsRolling resistancePenetration depthMetallurgyComposite materialGeometryPhysicsMathematicsOptics

Abstract

fetched live from OpenAlex

During the high hardness grinding balls production the following to the process parameters is the key requirement. The temperature and the deformation uniformity are main parameters at the high hardness grinding balls rolling. Reduction forces can be estimated by modeling, but in this case, they were determined analytically, based on the distribution of the real specific reduction force over the observed temperature fields on the surface of the ball. Revealed a significant increase in the temperature of ball during the cross-helical rolling from equator to the poles from 880 °С to 940 °С. Established temperature increase (up to 60 °C) was observed in the area of the roll flange penetration into the workpiece, where significant deformations occur. Maximum average power of rolling generated on the top of the roll flanges has been determined, which reaches 350 MPa and decreases linearly with the distance from the top of the flange of the deformation tool and is practically reduced to zero on the equator of the ball. It was shown that linear nature of the change of the specific power of rolling is in good agreement with the linear dependence of the mill roll flange growth when it is introduced into the workpiece.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.191
Teacher spread0.181 · 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.

Study designBench or experimental
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

Citations0
Published2019
Admission routes1
Has abstractyes

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