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Record W2912414919 · doi:10.1080/13621718.2019.1573011

Suppression of liquid metal embrittlement in resistance spot welding of TRIP steel

2019· article· en· W2912414919 on OpenAlexafffund
Liu He, Christopher DiGiovanni, Xu Han, C. W. Mehling, E. Wintjes, E. Biro, Y. Zhou

Bibliographic record

VenueScience and Technology of Welding & Joining · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersInternational Zinc AssociationCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLiquid metal embrittlementMaterials scienceMetallurgyAluminiumSpot weldingWeldingCoatingGalvannealedGalvanizationZincCorrosionEmbrittlementCrackingComposite materialMicrostructureLayer (electronics)Grain boundary

Abstract

fetched live from OpenAlex

Third-generation advanced high strength steels are typically given a zinc coating that provides excellent resistance to corrosion. During the resistance spot welding process, the melted zinc coating enables liquid metal embrittlement (LME) that causes cracking in the weld indent. In this study, LME in TRIP 1100 and TRIP 1200 steels was suppressed by placing aluminium interlayers added between the electrode and steel contact surface. Compared to welds exhibiting LME, TRIP 1100 with aluminium interlayers showed complete strength recovery while TRIP 1200 with aluminium interlayers resulted in a recovery of strength by 90%. Aluminium interlayers suppress LME by the formation of iron aluminides that hinder liquid zinc from coming in contact with the steel substrate, thus preventing LME.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

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