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Record W4401330997 · doi:10.29391/2024.103.029

Zinc-Induced Liquid Metal Embrittlement in Austenitic Microstructures

2024· article· en· W4401330997 on OpenAlexafffund
Ali Ghatei-Kalashami, Yaohe Zhou

Bibliographic record

VenueWelding Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersInternational Zinc AssociationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAmerican Welding Society
KeywordsLiquid metal embrittlementMetallurgyMaterials scienceAusteniteMicrostructureEmbrittlementZincLiquid metalMetalHydrogen embrittlementCorrosionGrain boundary

Abstract

fetched live from OpenAlex

The problem of liquid metal embrittlement (LME) is one of the main concerns to be addressed when developing high-strength automotive steels. Although LME has been extensively investigated over the past decade, the role of LME-induced cracking on the failure mechanism of steel substrates has not been adequately explored. This study investigates the influence of LME cracking on the tensile properties and failure mechanism of an austenitic microstructure. An in-depth analysis of the LME crack propagation path revealed that the crack propagated predominantly along highangle, random grain boundaries. The detailed failure analysis showed that the Zn-coated austenitic steel failed in an intergranular mechanism without any plastic strain being applied to the grains during deformation. The results of the study indicate that stress-assisted grain boundary diffusion is responsible for the premature failure of Zn-coated austenitic microstructures at much lower tensile stresses than that of the uncoated specimen. It was found that the application of thermomechanical conditions characterized by low stress and low temperature, combined with microstructures featuring a low Zn grain boundary diffusion rate, can effectively reduce LME cracking.

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 score0.030
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.261
Teacher spread0.251 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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