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Record W4220903900 · doi:10.1080/13621718.2022.2053395

Effect of beam wobbling on microstructure and hardness during laser welding of X70 pipeline steel

2022· article· en· W4220903900 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience and Technology of Welding & Joining · 2022
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsNatural Resources CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrostructureMaterials scienceWeldingMetallurgyPipeline (software)Laser beam weldingLaser beamsLaserComposite materialOpticsMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Laser welding with beam wobbling was employed to weld X70 pipeline steel. The influence of beam wobbling on weld surface morphology, fusion zone microstructure, and microhardness was investigated. The formation of spatter was suppressed by beam wobbling using a beam with higher energy density. At a welding speed of 1.0 m min −1 , the weld metal produced with a circular laser wobble pattern contained more martensite but less ferrite, compared to that of static spot welded joint. The presence of more martensite led to the increase in fusion zone hardness. A softened region was found in the inter-critical heat-affected zone. The hardness of the softened region could be improved by increasing the welding speed (from 1.0 to 1.5 m min −1 ).

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 score0.041
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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