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Record W4407636329 · doi:10.1016/j.jmapro.2025.02.021

Elucidation of alkali element's role in optimizing metal transfer behavior in rutile-type flux-cored arc welding

2025· article· en· W4407636329 on OpenAlexaff
Dang Khoi Le, Shinichi Tashiro, Ngoc Quang Trinh, Tetsuo Suga, Naoki Sawamura, Kazuhiro Fukuda, Shuji Sasakura, J. Eduardo Alvarez-Rocha, Patricio F. Méndez, Anthony B. Murphy, Van Hanh Bui, Manabu Tanaka

Bibliographic record

VenueJournal of Manufacturing Processes · 2025
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceAlkali metalRutileWeldingFlux (metallurgy)Arc (geometry)MetallurgySubmerged arc weldingArc weldingComposite materialChemical engineeringMechanical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigates the effects of alkali elements on metal transfer behavior in rutile flux-cored arc welding. Four types of prototype flux-cored wires with different sodium contents in the flux were fabricated. By using these wires, the influence mechanism of sodium on the metal transfer behavior was elucidated through shadowgraph measurements of the metal transfer behavior as well as spectroscopic and color image observations of the arc characteristics. It was found that the metal transfer between 190 A and 310 A was in the projected transfer mode and could be further classified into two sub-modes (type A and type B) based on the droplet formation process. A larger droplet was formed on the side of flux column in type A, while a smaller one was formed in the center covering the flux in type B. The metal transfer frequency became larger in the latter case for the same wire feeding speed. Type A tended to dominate in the lower current and lower sodium content conditions, while type B dominated in the opposite conditions. The dominant sub-mode was determined to depend on the Lorentz force acting on the droplet. At medium currents (250 A and 280 A), both sub-modes appeared in similar proportions. The maximum metal transfer frequency occurred at a particular sodium content. When the sodium content was smaller or larger, type A or type B became dominant, respectively. The sodium content at which the maximum frequency occurred decreased when the current increased. In type A, the iron plasma was widely distributed on the droplet side of the flux, while the sodium plasma was concentrated near the flux on the opposite side, so both were separated. In contrast, in type B, the sodium plasma was concentrated around the flux at the center and the iron plasma was widely distributed in the arc column, so both overlapped around the center. Sodium has a low boiling point and low ionization potential. In type A, the sodium vapor greatly increased the electrical conductivity of plasma around the flux column, so part of the current flowed from the wire through the sodium plasma to the weld pool. Accordingly, the current flowing through the bottom of the droplet to the arc decreased, leading to a lower arc pressure and recoil pressure under the droplet, and causing the metal transfer frequency to increase with sodium content. On the other hand, in type B, the sodium vaporization increased around the center, increasing the recoil pressure. In addition, the current density at the bottom of the droplet increased due to the current concentration in the arc, causing the arc pressure to rise. Therefore, the metal transfer frequency tended to decrease with sodium content. Due to the balance of these factors, the metal transfer frequency has a maximum at a particular sodium content.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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

Citations8
Published2025
Admission routes1
Has abstractyes

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