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Surface residual stresses in multipass welds produced using low transformation temperature filler alloys

2014· article· en· W2087500530 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Technology of Welding & Joining · 2014
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMaterials scienceResidual stressWeldingComposite materialUltimate tensile strengthNeutron diffractionCompression (physics)Fusion weldingPlasticityDiffractionMetallurgyOptics

Abstract

fetched live from OpenAlex

Tensile residual stresses at the surface of welded components are known to compromise fatigue resistance through the accelerated initiation of microcracks, especially at the weld toe. Inducement of compression in these regions is a common technique employed to enhance fatigue performance. Transformation plasticity has been established as a viable method to generate such compressive residual stresses in steel welds and exploits the phase transformation in welding filler alloys that transform at low temperature to compensate for accumulated thermal contraction strains. Neutron and X-ray diffraction have been used to determine the stress profiles that exist across the surface of plates welded with low transformation temperature welding alloys, with a particular focus on the stress at the weld toe. For the first time, near surface neutron diffraction data have shown the extent of local stress variation at the critical, fusion boundary location. Compression was evident for the three measurement orientations at the fusion boundaries. Compressive longitudinal residual stresses and tensile transverse stresses were measured in the weld metal.

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.

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)0.001
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 score0.049
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.236
Teacher spread0.227 · 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