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Record W4206062318 · doi:10.29391/2022.101.031

Capacitor Discharge Spot Welding of Aluminum, Part 2: Electrode Life Assessments

2021· article· en· W4206062318 on OpenAlexaff
Jerry E. Gould, Lindsey R. Lindamood, Julio Malpica, PATRICK LESTER, Dewei Zhu

Bibliographic record

VenueWelding Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsWeldingElectrodeSpot weldingMaterials scienceElectric resistance weldingWelding power supplyAnodeMetallurgyElectrical engineeringComposite materialArc weldingMechanical engineeringEngineeringFiller metalChemistry

Abstract

fetched live from OpenAlex

A major challenge for high-volume resistance spot welding of aluminum sheet is durability of the electrodes themselves. In production today, electrodes have total anticipated lives (including dressing) on the order of 1000 welds. This is largely related to the use of medium-frequency direct current (MFDC) power. The single-polarity orientation of MFDC welding results in excessive heating of one electrode (anode) and accelerated wear rates. Recently, technology employing capacitor discharge (CD) welding in conjunction with polarity switching has been developed. This work is the first effort in examining the response of resistance spot welding on aluminum sheet using this power source. Part 1 of this research (Ref. 1) described basic process robustness in spot welding with CD power systems. Part 2 addresses electrode life response. Duplicate electrode life tests were completed for 2000 welds without failure. These results were related to the polarity switching and short time that produced balanced and minimized wear. Additional testing was done without the use of electrode-cooling water. A limited test (500 welds) largely paralleled the ones done with cooling, suggesting that long-term spot welding with polarity-switching CD power and no water was possible.

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 categoriesMeta-epidemiology (narrow)
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 score0.335
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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.

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
Published2021
Admission routes1
Has abstractyes

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