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Welding behaviour, microstructure and mechanical properties of dissimilar resistance spot welds between galvannealed HSLA350 and DP600 steels

2009· article· en· W1998606999 on OpenAlexafffund
M. Shehryar Khan, S.D. Bhole, D.L. Chen, E. Biro, G. Boudreau, J. van Deventer

Bibliographic record

VenueScience and Technology of Welding & Joining · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsArcelorMittal (Canada)Toronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaArcelorMittal
KeywordsSpot weldingMaterials scienceGalvannealedWeldingMicrostructureMetallurgyUltimate tensile strengthHeat-affected zoneMartensiteComposite materialGalvanization

Abstract

fetched live from OpenAlex

Resistance spot welding experiments were conducted on dissimilar material combination of HSLA350/DP600 steels. The welds were characterised using optical and scanning electron microscopy. The fusion zone of the dissimilar material spot weld was predominantly martensitic with some bainite. Mechanical properties were also determined by tensile shear, cross tension and fatigue tests. The performance of dissimilar material spot weld was different from that of the similar ones in each of the HSLA350 and DP600 steels and exhibited different heat affected zone hardness. The DP600 weld properties played a dominating role in the microstructure and tensile properties of the dissimilar material spot welds. However, the fatigue performance of the dissimilar welds was similar to that of the HSLA350 welds. Fatigue tests on the dissimilar material spot welds showed that the 5·5 mm diameter nugget exhibited higher fatigue strength than the 7·5 mm diameter nugget.

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 score0.005

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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.222 · 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

Citations67
Published2009
Admission routes2
Has abstractyes

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