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Record W4412449849 · doi:10.1016/j.msea.2025.148781

Localization of plastic deformation at weld seams of porthole die Al-Mg-Si extrusions

2025· article· en· W4412449849 on OpenAlexafffund
Andrew Zang, Jean-François Béland, Ali Khajezade, Nick Parson, Warren J. Poole

Bibliographic record

VenueMaterials Science and Engineering A · 2025
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsAluminium Refining, Degassing and Filtering (Canada)University of British Columbia
FundersRio TintoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Research Council CanadaFord Motor Company
KeywordsDie (integrated circuit)Deformation (meteorology)Materials scienceWeldingMetallurgyGeologyComposite material

Abstract

fetched live from OpenAlex

This study investigates the localization of plastic strain near the weld seams of porthole die extruded Al-Mg-Si alloys with primarily unrecrystallized microstructures, relevant to post-forming or crash scenarios. A link was established between the crystallographic texture and the plastic response of the material at a local level. Optical metallography and electron backscatter diffraction (EBSD) were used to systematically characterize the microstructure and textures in the extrudates. It was found that a variation in bridge geometry produced very different patterns of textures near the weld seams. To determine the effect of the texture patterns, a slip system level polycrystal plasticity code was used to simulate the mechanical response for each region of similar texture. The predicted properties were then used to fit a Barlat YLD2004-18p anisotropic yield function, and finite element method (FEM) simulations were conducted using the yield functions as inputs. The results revealed a match between the simulated patterns of strain localization and the experimental strain patterns observed via a micro-scale digital image correlation (DIC) technique for each bridge case. These findings establish crystallographic texture as a primary factor affecting the mechanical behaviour of extruded profiles, opening the door to influencing the weld seam properties through the control of crystallographic texture using die bridge design.

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 score0.026
Threshold uncertainty score0.318

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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

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