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Record W2957789654 · doi:10.1115/1.4044253

Evaluation of Electrode Degradation and Projection Weld Strength in the Joining of Steel Nuts to Galvanized Advanced High Strength Steel

2019· article· en· W2957789654 on OpenAlexafffund
Pablo D. Enrique, Hashem Al Momani, Christopher DiGiovanni, Zhen Jiao, K. Chan, Y. Zhou

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsHuys Industries (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWeldingMaterials scienceGalvanizationSpot weldingElectrodeMetallurgyTungstenComposite materialElectric resistance weldingLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

Abstract Projection welding of steel weld nuts to advanced high strength steel (AHSS) in automotive applications allows for the reliable mounting of critical components with different thicknesses to the vehicle body. However, the galvanized coatings commonly used on AHSS result in electrode surface degradation during welding. In this study, the electrode degradation and its effect on the mechanical properties of welded steel nuts and AHSS sheets are investigated. Two common electrode materials are tungsten/copper and beryllium-free class III copper—both display the formation of an oxidized alloy surface layer and pitting as weld number increases. Unlike resistance spot welding, where electrodes grow in the contact area diameter as they degrade, projection welding electrodes do not experience this type of mechanical degradation. Instead, increased resistance at the electrode interface with increasing weld number results in higher temperatures at the weld interface and a larger fusion zone size, which is responsible for an observed 30% increase in weld strength over the span of 10,000 welds.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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