MétaCan
Menu
Back to cohort
Record W4408857829 · doi:10.1016/j.jmrt.2025.03.220

The influence of bridge geometry and welding chamber height on microstructure and mechanical properties for porthole die extrusion of AA6082

2025· article· en· W4408857829 on OpenAlexafffund
Yu Wang, Andrew Zang, Mary A. Wells, Warren J. Poole, Nick Parson

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsAluminium Refining, Degassing and Filtering (Canada)University of British ColumbiaUniversity of Waterloo
FundersRio TintoNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceExtrusionDie (integrated circuit)MicrostructureWeldingAlloyMetallurgyBridge (graph theory)Composite materialNanotechnology

Abstract

fetched live from OpenAlex

This study investigates the effects of bridge geometry and welding chamber height on the microstructure and mechanical properties of AA6082 aluminum extrusions using electron backscatter diffraction (EBSD), tensile testing, and numerical simulations. EBSD results showed that a shallow welding chamber with a flat bridge geometry produced CubeRd (∼40 %), Cube (∼15 %) and Goss (∼15 %) texture components along the welding seam, whereas the corresponding texture was mainly Copper (50–80 %) for material extruded using the other three geometries, i.e., shallow streamlined, deep streamlined and deep flat. Tensile tests using digital image correlation (DIC) revealed strain localization at the weld seam for all geometries, with the shallow flat die exhibiting the most severe peak strain (∼0.22 at a far-field strain of 0.12) compared to ∼0.16 for streamlined bridges. Increasing the welding chamber height for the streamlined bridge had almost no effect on the mechanical properties of the extruded sample. However, in the flat bridge case, the same increase significantly altered both the texture and mechanical properties. DEFORM® (Design Environment for FORMing) 3D simulations of the thermal-mechanical history showed that the shallow flat die extrusion experienced a higher exit temperature and effective plastic strain compared to the other die designs which allow for the rationalization of the texture differences between the different dies.

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)0.001
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 score0.004
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.289
Teacher spread0.267 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Materials Research and TechnologySame topicMetallurgy and Material FormingFrench-language works237,207