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Record W2140242667 · doi:10.5267/j.esm.2014.7.001

Enhancement of fatigue life of rail-end-bolt holes by slide diamond burnishing

2014· article· en· W2140242667 on OpenAlexvenueno aff
Jordan Maximov, Galya Duncheva, Angel Anchev, I. M. Amudjev, V. T. Kuzmanov

Bibliographic record

VenueEngineering Solid Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsnot available
FundersTehnicheski universitet Gabrovo
KeywordsBurnishing (metal)Materials scienceDiamondStructural engineeringMetallurgyComposite materialForensic engineeringMechanical engineeringEngineeringPolishing

Abstract

fetched live from OpenAlex

The fatigue failure around rail-end-bolt holes is particularly dangerous since it leads to derailment of trains and consequently to inevitable accidents. It is well-known that the fatigue life of structural holed components, subjected to cyclic load, can be increased by generating compressive hoop stresses around the holes. These beneficial residual compressive stresses significantly reduce the maximum values of the operating tensile stresses arising at the critical points of the components and thus impede the formation of first mode cracks. A new approach to enhancement of fatigue life of rail-end-bolt holes has been developed. The approach involves sequential drilling and reaming through a new combined tool and then slide diamond burnishing by a new device. The technology implementation was carried out on machine tool. The process of creating residual stresses has been studied both experimentally and numerically. The experimental study was conducted by means of a modified split ring method. A reliable finite element modeling approach to the slide diamond burnishing process was developed. On this basis, the process was optimized by means of a genetic algorithm. As a result, the optimal combination of the governing process parameters is established, which ensures both maximum depth of the compressive zone and maximum absolute values of the residual stresses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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