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Improving weld strength of magnesium to aluminium dissimilar joints via tin interlayer during ultrasonic spot welding

2012· article· en· W2046468000 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience and Technology of Welding & Joining · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMetallurgyWeldingTinIntermetallicAluminiumBrittlenessUltimate tensile strengthSpot welding5052 aluminium alloy6063 aluminium alloyComposite materialAlloy6111 aluminium alloy

Abstract

fetched live from OpenAlex

Welding of magnesium to aluminium alloys is enormously challenging due to the formation of brittle Al 12 Mg 17 intermetallic compounds (IMCs). This study was aimed at improving the strength of dissimilar joints of AZ31B-H24 magnesium alloy to 5754-O aluminium alloy by using a tin interlayer inserted in between the faying surfaces during ultrasonic spot welding. The addition of tin interlayer was observed to successfully eliminate the brittle Al 12 Mg 17 IMCs, which were replaced by a layer of composite-like tin and Mg 2 Sn structure. Failure during the tensile lap shear tests occurred through the interior of the blended interlayer as revealed by X-ray diffraction and SEM observations. As a result, the addition of a tin interlayer resulted in a significant improvement in both joint strength and failure energy of magnesium to aluminium dissimilar joints and also led to an energy saving because the optimal welding energy required to achieve the highest strength decreased from ∼1250 to ∼1000 J.

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.

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)0.001
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 score0.051
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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