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Record W1512880707 · doi:10.4271/2005-01-0904

Performance Evaluation of GMAW Welds for Four Advanced High Strength Steels

2005· article· en· W1512880707 on OpenAlexaff
Benda Yan, Sree Harsha Lalam, Hongtao Zhu

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsMitel (Canada)
FundersBattelle
KeywordsMaterials scienceWeldingMetallurgyMartensiteGas metal arc weldingDual-phase steelSofteningIndentation hardnessMicrostructureBase metalFatigue limitJoint (building)Heat-affected zoneComposite materialStructural engineering

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">This paper investigates the characteristics of GMAW of various sheet steels grades ranging from HSLA, dual phase, to martensitic. From the arc welding point of view, the dual phase and martensitic steels behave similarly to conventional high strength steels. Regarding the properties of GMAW joints, the static and dynamic mechanical testing were conducted and compared along with the weld metal microhardness and microstructure. Results show that while the strength of the sheet steel weld, in general increases with the base material strength, Joint Efficiency, defined as the ratio of the strength of joint to the strength of the base metal, decreases with the increase of martensite fraction in the sheet steel. Martensitic steels, especially, exhibits reduced weld strength due to softening of the HAZ. However, fatigue strength of these steels is not adversely affected by the softened HAZ, and is insensitive to the strength of the steel. The hardness distribution in weldments can significantly affect the static performance of the welds and is dependent on the steel chemistry, steel processing and filler metal.</div>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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.

Study designObservational
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

Citations23
Published2005
Admission routes1
Has abstractyes

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