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Record W3117253764 · doi:10.18280/acsm.440602

Microstructural and Mechanical Characterization of Shielded Metal Arc Welded Dual Phase Steel Joints

2020· article· en· W3117253764 on OpenAlexvenueno aff
Walid Laroui, Redouane Chegroune, Şükrü Talaş, М. Кеддам, Riad Badji

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceMetallurgyMartensiteWeldingUltimate tensile strengthDuctility (Earth science)MicrostructureQuenching (fluorescence)Dual-phase steelFerrite (magnet)Volume fractionFormabilityHeat-affected zoneComposite materialCreep

Abstract

fetched live from OpenAlex

Dual Phase steels are widely used in industry for various reasons for their improved mechanical properties owing to their ferrite and martensite contents in microstructure and good formability at industrial scale to achieve final shape with good ductility. In this study, shielded metal arc welding experiments were performed in order to evaluate the metallurgically and mechanically the properties of Dual Phase steels obtained from plain carbon steel AISI 1010 by water quenching at intercritical temperatures. Martensite volume fraction (MVF) was related to the intercritical quenching temperature which defines the mechanical properties of Dual Phase steel. All the welding parameters were kept constant in order to get a value of heat input equals 1.37 KJ/mm for all weldments. Martensite was found to be predominated in the fusion zone and its hardness was the highest compared to base metal (BM) and heat affected zone (HAZ). The extent of ductile zone was found to be dependent on the MVF and reached over 1.2 mm wide. Tensile properties of weldments were deteriorated by 35% in ultimate tensile strength (UTS) and by 15% in elongation. The failure of all the welded joints was occurred in the SC zone, with the fractured surfaces of a dimple feature.

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 score0.015
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.247
Teacher spread0.213 · 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
Published2020
Admission routes1
Has abstractyes

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