MétaCan
Menu
Back to cohort
Record W1966306881 · doi:10.1115/gt2004-53925

Numerical Simulation of the Low Plasticity Burnishing Process for Fatigue Property Enhancement

2004· article· en· W1966306881 on OpenAlexafffund
W. Bereś, Junyi Li, Prakash Patnaik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsResidual stressBurnishing (metal)Materials scienceFinite element methodPlasticityParametric statisticsBall (mathematics)Structural engineeringResidualComposite materialMechanicsMechanical engineeringComputer scienceGeometryAlgorithmEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

Low Plasticity Burnishing (LPB) process introduces a deep layer of high-compression material through an application of a smooth, free-rolling ball moving in a single pass parallel to the material surface. This paper describes modelling the LPB process numerically using two- and three-dimensional finite element method, and reports the results of parametric studies. The residual stress distributions, as well as the total and inplane plastic strains obtained from the numerical calculations, are compared to the published results of residual stress measurements. In addition, the preliminary results of the finite element modelling of residual stress relaxations caused by the elevated temperatures are also reported.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.141

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 head0.030
GPT teacher head0.275
Teacher spread0.244 · 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

Citations7
Published2004
Admission routes2
Has abstractyes

Explore more

Same topicSurface Treatment and Residual StressFrench-language works237,207