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Record W4388554167

Laboratory and Rig Tests of High-Strength and High Crack Resistance Wheel Tyres

2015· article· en· W4388554167 on OpenAlexaff
G. I. Bryunchukov, А. С. Разумов, A. V. Sukhov, А. В. Зорин, Р. А. Ильиных, V. V. Brekson

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsMaterials scienceStructural engineeringForensic engineeringEngineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

The paper presents development/comprehension tests outcomes of locomotive wheel tyre prototypes of grade ‘H’ medium alloy steel. These high-strength and high crack resistance tyres are intended for use with series 2EC10, 2EC7 and 2EC6 new generation freight electric locomotives with asynchronous traction drive. The work under discussion was carried out by the JSC VNIIZhT experts in cooperation with JSC “EVRAZ NTMK”. It covered the development of wheel tyre production specifications including those related to chemical content and mechanical properties of steel, appraisal of the tyre heat treatment conditions, manufacturing of tyre prototypes as well as laboratory and rig testing aimed to assess the tyre property package. Wheel tyre prototypes of grade “H” steel were subject to laboratory and rig tests at the JSC VNIIZhT experimental facilities inclusive of analysis of working layer mechanical properties and of hardness/microstructure distribution over the cross-section, assessment of cold resistance, determination of the tyre static fracture toughness (crack resistance) K1С and damping capacity (tenacity) Kfc, as well as analysis of the tyre stress state. Based on the positive results of the initial and acceptance tests of the grade ‘H’ steel tyres pilot run there was adopted the decision on the under-control operation of the tyres with the series 2EC10 electric locomotives on the Sverdlovskaya Railway network.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.147
GPT teacher head0.481
Teacher spread0.334 · 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 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
Published2015
Admission routes1
Has abstractyes

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