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Record W3119896879 · doi:10.3390/met11010120

Effect of Asymmetric Material Flow on the Microstructure and Mechanical Properties of 5A06 Al-Alloy Welded Joint by VPPA Welding

2021· article· en· W3119896879 on OpenAlexaff
Zhaoyang Yan, Shujun Chen, Fan Jiang, Xing Zheng, Ooi Tian, Xinqiang Ma

Bibliographic record

VenueMetals · 2021
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of China
KeywordsWeldingMaterials scienceMicrostructureComposite materialJoint (building)Ultimate tensile strengthGrain sizeTexture (cosmology)MetallurgyAlloyBase metalGrain boundaryStructural engineering

Abstract

fetched live from OpenAlex

The microstructure, texture, and mechanical properties of the asymmetric welded joint in variable polarity plasma arc (VPPA) welding were studied and discussed in this paper. The asymmetric welded joint was obtained through horizontal welding, where the effect of gravity caused asymmetric material flow. The results showed that the grain size and low angle grain boundary (LAGB) at both sides of the obtained welded joint were asymmetric; the grain size differed by a factor of 1.3. The average grain size of the Base Metal (BM), Lower-weld zone (WZ) and Upper-WZ were 25.73 ± 1.25, 37.87 ± 1.89 and 49.92 ± 2.49 µm, respectively. There is discrepancy between the main textures in both sides of the welded joint. However, the effect of asymmetric metal flow on the weld texture was not significant. The micro-hardness distribution was inhomogeneous, the lowest hardness was observed in regions with larger grain sizes and smaller low angle grain boundary. During tensile strength tests, the specimens fractured at the position with the lowest hardness although it has reached 89.2% of the strength of the BM. Furthermore, the effect of asymmetric metal flow and underlying causes of asymmetric weld properties in VPPA horizontal welding have been discussed and analyzed.

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 score0.003
Threshold uncertainty score0.322

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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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