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Record W3003778805 · doi:10.1016/j.jmrt.2020.01.040

Weld properties and residual stresses of VPPA Al welds at varying welding positions

2020· article· en· W3003778805 on OpenAlexaff
Zhaoyang Yan, Shujun Chen, Fan Jiang, Ooi Tian, Ning Huang, Suolai Zhang

Bibliographic record

VenueJournal of Materials Research and Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Alberta
FundersBeijing Municipal Natural Science FoundationNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of China
KeywordsWeldingMaterials scienceResidual stressHeat-affected zoneComposite materialUltimate tensile strengthMetallurgyElectric resistance welding

Abstract

fetched live from OpenAlex

The micro-structure and residual stresses of VPPA welds at varying welding positions still lack systematic research. To clarify the effects of gravity on Al weld performance, 7 welding positions (from vertical-up welding to horizontal welding) were used to investigate the evolution of microstructure, mechanical properties and residual stresses. The results showed that both micro-structure and residual stresses of the welded joints in non-vertical up welding were asymmetric while those in vertical up welding were symmetric. The grain size on the upper side of the welded joints increased from about 66.9 µm for vertical up welding to 113.9 µm for horizontal welding. Furthermore, though lots of tear ridges and dimples appeared in all the fracture surfaces, which showed ductile fracture, all fractures for non-vertical up welding appeared on the upper side of the weld. The tensile strength from vertical up welding to horizontal welding decreased from 352.8 MPa to 319.8 MPa. In this paper, the formation mechanisms and underlying cause of the asymmetric residual stresses and mechanical properties in non-vertical up welding have been analysed and discussed.

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)0.000
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 score0.327

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.064
GPT teacher head0.307
Teacher spread0.243 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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