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Record W2890926923 · doi:10.1080/13621718.2018.1518363

Impact of liquid metal embrittlement cracks on resistance spot weld static strength

2018· article· en· W2890926923 on OpenAlexafffund
Christopher DiGiovanni, E. Biro, Y. Zhou

Bibliographic record

VenueScience and Technology of Welding & Joining · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaInternational Zinc Association
KeywordsMaterials scienceLiquid metal embrittlementEmbrittlementUltimate tensile strengthWeldingMetallurgyMicrostructureFracture (geology)Composite materialSpot weldingZincGrain boundary

Abstract

fetched live from OpenAlex

Advanced high strength steels used in automotive structural components are commonly protected using zinc coatings. However, the steel/zinc system creates the potential for liquid metal embrittlement (LME) during welding. Although LME cracks are known to form, limited research has found any detrimental impact of LME cracks on weld strength. In this work, a comparison of zinc coated and uncoated advanced high strength steel joints showed LME decreased strength in welds from transformation induced plasticity type microstructures and an 1100 MPa ultimate tensile strength by 43.6%. LME cracks were observed to propagate until final fracture. However, only cracks located in the periphery of the weld area were found contribute to a loss in strength.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations82
Published2018
Admission routes2
Has abstractyes

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