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Record W4406370991 · doi:10.1016/j.jma.2024.12.022

Surface properties of friction stir welded dissimilar joints of AA7075 and Mg-WE43 alloys: Effect of positional arrangement

2025· article· en· W4406370991 on OpenAlexaff
Tariq Ahmad, Nadeem Fayaz Lone, Noor Zaman Khan, Babar Ahmad, Arshad Noor Siddiquee, D.L. Chen

Bibliographic record

VenueJournal of Magnesium and Alloys · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceFriction stir weldingWeldingMetallurgyComposite material

Abstract

fetched live from OpenAlex

• The average grain size of the al/mg joint is reduced from 4.11 to 2.04 µm. • The wear rate decreased from 0.41 mm³/Nm to 0.27 mm³/Nm. • The corrosion resistance is improved from 77.59 to 56.299 mpy. • The reduction in average microhardness was achieved by reduced IMC formation. This study investigates the surface properties of the dissimilar AA7075/Mg-WE43 joints wherein the effect of positioning the AA7075 on the advancing or retreating sides during friction stir welding (FSW) was scrutinized. The EBSD analysis revealed that the average grain size reduced from 4.11 ± 0.7 µm to 2.04 ± 0.9 µm when AA7075 was shifted from advancing to retreating side. The results showed that positioning AA7075 on the retreating side significantly reduced the wear rate from 0.41 mm³/Nm to 0.27 mm³/Nm and mitigates the problem of brittle intermetallic compound (IMC) formation, consequently reducing the average microhardness. Corrosion resistance improved from 77.59 mpy to 56.299 mpy. The higher grain refinement improved wear resistance due to higher grain boundary density and greater chances of formation of protective oxide films. The current work thus enhances the practical applicability of AA7075/Mg-WE43 welds for lightweighting of automotive structures.

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 score0.014
Threshold uncertainty score0.398

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations14
Published2025
Admission routes1
Has abstractyes

Explore more

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