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Record W2887011958 · doi:10.1520/mpc20170125

Experimental Measurement of Residual Stresses in Cr-Mo-V Steel Restrained Welds with High Thickness

2018· article· en· W2887011958 on OpenAlexaff
N. Maestri, Paolo Marangoni, D. Pettene, B. Rivolta, Riccardo Gerosa

Bibliographic record

VenueMaterials Performance and Characterization · 2018
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsMaterials scienceResidual stressWeldingHole drilling methodNozzleGroove (engineering)Composite materialMetallurgySubmerged arc weldingDeep hole drillingDrillingArc weldingMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This experimental work is focused on the residual stresses induced by multi-pass welding of thick components. Two samples were produced, inspired by the nozzle-vessel geometry, with Submerged Arc Welding buttwelding, performed according to the standard welding procedures employed by Belleli Energy CPE. The components were characterized by different sizes and groove positions. Measurements of residual stresses were carried out by hole drilling according to ASTM E837-13a, Standard Test Method for Determining Residual Stresses by the Hole-Drilling Strain-Gage Method, in different positions of the samples. The measurements were performed on welded, and Dehydrogenation Heat Treatment (350°C, 4h), and Intermediate Stress Relieving treatments. The obtained results allowed a discussion of the influence of the component size on the residual stresses and the effectiveness of an intermediate heat treatment for reducing the stress state.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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