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Record W2996907044 · doi:10.5539/jmsr.v9n1p32

Vibratory Stress Relief and Vibratory Weld Conditioning of Flux cored arc welded CA6NM steel

2019· article· en· W2996907044 on OpenAlexvenueno aff
Luiz Fernando Cursino Briet de Almeida, Júlio César Lourenço, Maria Ismênia Sodero Toledo Faria, Décio Vieira, A. Robin, Carlos Ângelo Nunes

Bibliographic record

VenueJournal of Materials Science Research · 2019
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsWeldingMaterials scienceCharpy impact testMartensiteMetallurgyHeat-affected zoneMicrostructureJoint (building)Composite materialStructural engineering

Abstract

fetched live from OpenAlex

ASTM A743 CA6NM steel is used in the manufacturing of hydraulic turbines components. Multipass welding is commonly used for their fabrication or repairing. In this work, two different vibratory welding procedures were studied: vibration applied during welding (VWC) and vibration applied after welding (VSR). Results have shown that in both conditions, CA6NM steel presented a martensitic microstructure, in which the VSR welded joint presented column-shaped packets and fine martensite delineating the individual beads, while VWC joint presented grain refinement. Heat affected zones (HAZ) presented δ-phase in small amounts for both conditions in the regions which reached higher temperatures. VSR and VWC conditions presented similar behavior in terms of hardness, HAZ hardness values being close to those of the weld metal, except for the root regions, where higher values were obtained. Charpy-V results showed that HAZs presented higher impact values than those of the weld metal. The low impact values of the weld metal were attributed to presence of inclusions from the welding electrode.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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