MétaCan
Menu
Back to cohort
Record W4408652257 · doi:10.1080/10426914.2025.2469543

Processing characteristics of Ni-WC MMC weld cladding by various types of GMAW

2025· article· en· W4408652257 on OpenAlexaff
Mohammad Reza Karimi, Sheng-Hui Wang, Jasmin Jelovica

Bibliographic record

VenueMaterials and Manufacturing Processes · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceWeldingMetallurgyCladding (metalworking)Gas metal arc weldingWeld poolComposite materialGas tungsten arc weldingLaser beam weldingArc welding

Abstract

fetched live from OpenAlex

GMAW (gas metal arc welding) with various transfer modes, including GMAW-G (GMAW with globular transfer), GMAW-S (GMAW with short-circuit transfer), GMAW-P (pulsed GMAW), and CMT (cold metal transfer), were engaged for depositing overlays of nickel-tungsten carbide composites. For CMT and GMAW-S, the current/voltage cycle and droplet transfer frequencies consistently aligned with each other, and weld droplets were transferred by short circuiting. For GMAW-P, droplet transfer frequency was lower than current/voltage cycle frequency, and altering wire feed speed or shielding gas composition induced a transition between pulsed-spray and short-circuit transfer. For GMAW-P and GMAW-G, shielding gas with 25% CO2 reduced the arc intensity and arc length, promoting arc relocation to the bottom of a weld droplet and short-circuit transfer, in contrast to shielding gas with 2% O2. Assessments of current/voltage probability density function curves and fast Fourier transform analysis can offer compelling insights regarding the characteristics of metal transfer and/or processing stability.

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 score0.003
Threshold uncertainty score0.006

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 head0.004
GPT teacher head0.199
Teacher spread0.196 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueMaterials and Manufacturing ProcessesSame topicAdvanced materials and compositesFrench-language works237,207