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Record W2407188189 · doi:10.1080/13621718.2016.1180844

Electrode life of aluminium resistance spot welding in automotive applications: a survey

2016· article· en· W2407188189 on OpenAlexafffund
W. J. Zhang, Isidore Cross, Paul L. Feldman, Sajja Rama, Sarah Norman, Marco Del Duca

Bibliographic record

VenueScience and Technology of Welding & Joining · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsPROTO Manufacturing (Canada)
FundersUniversity of Waterloo
KeywordsSpot weldingMaterials scienceElectrodeWeldingAluminiumElectric resistance weldingMetallurgyElectrical contactsContact resistanceComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

Short electrode life has been one of the crucial issues for aluminium resistance spot welding in high-volume automobile manufacturing. A fair amount of heat may be generated at the electrode/workpiece interface because of the high electrical contact resistance of the aluminium oxide layers. Correspondingly, rapid electrode degradation is observed due to severe Cu–Al alloying, pitting and cavity during welding. This issue has not yet been fully addressed. This survey focuses on the current studies of understanding and development of electrode life improvement specifically on those that extend electrode life via optimising the electrical contact resistance at the interface, including altering the surface conditions of the workpiece, weld parameters development and weld equipment selections, and new electrode designs, and etc.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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 designObservational
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

Citations27
Published2016
Admission routes2
Has abstractyes

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