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Comparative study of microstructure and mechanical properties of laser welded–brazed Mg/steel joints with four different coating surfaces

2013· article· en· W2040492502 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 · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersChina Scholarship CouncilUniversity of Waterloo
KeywordsMaterials scienceCoatingMicrostructureBrazingLayer (electronics)WeldingUltimate tensile strengthPhase (matter)MetallurgyWettingComposite materialJoint (building)AluminiumCrackingAlloy

Abstract

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This paper presents a comparative study of laser welding–brazing Mg to steel with four different coating surfaces, including (Zn+pre-existing Fe–Al phase), pure Zn coating, pre-existing Fe–Al phase and fresh steel without any coating. The presence of Zn coating was found to significantly improve the wettability of liquid filler on steel. However, Mg–Zn products enriching at the seam head tended to cause cracking. The weak bonding of Mg–Zn products and Fe–Al layer was mainly responsible for the decreased tensile strength and interfacial failure that occurred in joints with the first two coatings. For joints with the latter two coatings, the thickness of newly formed Fe–Al layer determined the mechanical properties. The reaction layer formed at the Mg/fresh steel was thin, inducing interfacial failure, whereas the joint with pre-existing Fe–Al phase fractured at the seam, indicating that the pre-existing Fe–Al phase was beneficial to formation and growth of the Fe–Al phase.

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)0.000
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 score0.024
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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