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Measurements of deposition rate and droplet temperature in GMAW of aluminum alloys

2023· article· en· W4377019151 on OpenAlexaff
Rishiekesh Ramgopal, Zhaoyang Yan, Kevin S. Scott, Shujun Chen, Patricio F. Méndez

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGas metal arc weldingJoule heatingMaterials scienceDeposition (geology)AluminiumMetallurgyConsumablesWeldingAlloyArc (geometry)Arc weldingComposite materialChemistryMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This article explores the reason behind the higher deposition rate in Mg-containing aluminum alloys in gas metal arc welding (GMAW). Experiments performed on ER1100 (Mg-free alloy) and ER5183 (high Mg alloys) measured current, wire feed speed, and droplet temperature. A non-linear analysis of Joule heating at the electrode extension and an analysis of the power needed to heat the consumable to the droplet temperature were performed. Analysis revealed that Joule heating is two orders of magnitude smaller than what was necessary for the higher wire feed speed. The droplet temperature measurements were done for the first time for both consumables under identical conditions, which revealed that ER5183 had a lower droplet temperature than ER1100 by almost 350 K.

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 score0.017
Threshold uncertainty score0.388

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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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