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Record W3213974813 · doi:10.3390/met11111813

Investigation of Liquid Metal Embrittlement during Resistance Spot Welding of Martensitic Steel with Zn Jet Vapor-Deposited Coating

2021· article· en· W3213974813 on OpenAlexaff
Vojtěch Kučera, Zuzana Žofková, Christopher DiGiovanni, Liu He, Dalibor Vojtěch

Bibliographic record

VenueMetals · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceLiquid metal embrittlementSpot weldingMetallurgyWeldingCoatingUltimate tensile strengthEmbrittlementMartensiteComposite materialGalvannealedGalvanizationMicrostructureLayer (electronics)Grain boundary

Abstract

fetched live from OpenAlex

Advanced high-strength steels protected by zinc coatings have contributed to a reduction in CO2 emissions in the automotive industry. However, the liquid metal embrittlement (LME) of the Fe/Zn couple induced by simultaneously acting stresses and high temperatures during resistance spot welding could be the cause of unexpected failure. We investigated the possible risk of LME in spot-welded martensitic steel with Zn jet vapor-deposited coating and its influence on weld strength. The weld nugget cross-sections were analyzed (optical microscopy, SEM-EDS), and their tensile shear strengths were compared with their uncoated counterparts. LME cracks were observed in all samples meeting the process window (6, 6.5, 7 kA) located at the edge of the sheet/electrode indentation area. The frequency and length of cracks increased with current, and the occurrence of Zn within cracks indicated the LME mechanism. The shear tests showed the Zn-coated sample underwent a decrease in tensile shear strength that was most evident at a welding current of 7 kA (13.2%). However, LME was excluded as a cause of lower strength. The decrease was attributed to the smaller nugget diameter and the thin slit of Zn coating remaining in the weld notch.

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 score0.074
Threshold uncertainty score0.726

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 studies0.0000.000
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 head0.014
GPT teacher head0.223
Teacher spread0.208 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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