MétaCan
Menu
Back to cohort

Transient liquid phase (TLP) brazing of Mg–AZ31 and Ti–6Al–4V using Ni and Cu sandwich foils

2014· article· en· W2023370821 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 · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaGerman-Jordanian University
KeywordsMaterials scienceBrazingAlloyIsothermal processDissolutionJoint (building)Shear strength (soil)MetallurgyPhase (matter)MicrostructureComposite materialDiffusionLiquid phaseChemical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Transient liquid phase (TLP) brazing of Mg–AZ31 alloy and Ti–6Al–4V alloy was performed using double Ni and Cu sandwich foils. Two configurations were tested; first, Mg–AZ31/Cu–Ni/Ti–6Al–4V and second, Mg–AZ31/Ni–Cu/Ti–6Al–4V. The effect of set-up configuration of the foils on microstructural developments, mechanical properties and mechanism of joint formation was examined. The results showed that different reaction layers formed inside the joint region depending on the configuration chosen. The formation of ϵ phase (Mg), ρ (CuMg 2 ), δ (Mg 2 Ni) and Mg 3 AlNi 2 was observed in both configurations. Maximum shear strength obtained was 57 MPa for Mg–AZ31/Ni–Cu/Ti–6Al–4V configuration and in both configurations, the increase in bonding time resulted in a decrease in joint strength to 13 MPa. The mechanism of joint formation includes three stages; solid state diffusion, dissolution and widening of the joint, and isothermal solidification.

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)0.000
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 score0.012
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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