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Arc length behaviour and voltage predictions for GMAW-Sp aluminum procedures in the Lincoln Procedure Hand Book

2023· article· en· W4377019299 on OpenAlexaff
Jose Rocha, 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
KeywordsVoltageGas metal arc weldingWeldingArc (geometry)MechanicsArc lengthMaterials scienceElectrical engineeringCathodeElectrodeAnodeMetallurgyMechanical engineeringChemistryArc weldingEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Heat input is a key component of a welding procedure, which is dependent on the several fall voltages composing a total voltage loss. As current practice is primarily reliant on trial and error to determine voltage settings for a desired heat input, a means to understand and predict the voltage loss is of interest to welding engineers. Voltage, amperage, and arc length measurements using ER4043 1.2 mm at varying voltages were used to break down fall voltage constituents for a given weld with GMAW-Sp. Arc length was defined as the distance from the weld pool to the point where the metal vapour and ionized gas boundary attach to the consumable, and measured over 5 droplet cycles to obtain a time average. Aluminum procedures in the Lincoln Procedure Hand Book (LPHB) with 1.2, 1.6, and 2.4 mm consumables were analyzed to predict the individual fall voltage constituents using experimental results. Expected arc lengths ranged from 13-26 mm depending on voltage prescribed in the procedure, and agreed with comparative experiments. Combined anode/cathode, and the arc column were major contributors to overall fall voltage with 64±3%, and 34±3%, respectively. Contact tip, electrode extension, and lead cables were minor contributors, each contributing less than 1.2% to the overall voltage loss.

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 score0.072
Threshold uncertainty score0.547

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 communication0.0010.001
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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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