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Record W1936481888

Traitement de surfaces par impacts: Évaluation des contraintes résiduelles induites par martelage

2008· preprint· fr· W1936481888 on OpenAlexafffund
Lyès Hacini, Van Ngan Lê, Philippe Bocher

Bibliographic record

VenueDSpace (Centre National De La Recherche Scientifique) · 2008
Typepreprint
Languagefr
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsPeeningHammerMaterials scienceResidual stressCrackingMetallurgyComposite material
DOInot available

Abstract

fetched live from OpenAlex

Ce travail a pour objectif l'étude de la technique de relaxation des contraintes résiduelles par martelage. Comme première approche, on a choisi d'étudier l'effet du martelage seul sur des plaques libres. 4 plaques d'acier inoxydable austénitique 304L ont été martelées par 1, 3, 5 et 9 couches de martelage respectivement, alors qu'une cinquième plaque issue du même lot a servi de témoin pour évaluer les contraintes initiales. Les contraintes résiduelles ont été évaluées grâce à la technique des contours, qui permet d'avoir une carte 2D de la distribution des contraintes résiduelles dans le sens perpendiculaire au cordon de soudage. Ces essais ont permis de démontrer l'effet bénéfique du martelage à induire des contraintes de compression sur une profondeur de quelques millimètres, ce qui réduit les risques de fissuration, ainsi qu'une amélioration des propriétés mécaniques locales. Aussi, nous avons réussi à identifier le nombre optimal de couches de martelage à appliquer afin d'induire un niveau maximal de contraintes de compression tout en minimisant le nombre de passes de martelage et aussi le temps de traitement.

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)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.145
GPT teacher head0.341
Teacher spread0.196 · 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 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

Citations2
Published2008
Admission routes2
Has abstractyes

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