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Record W2995663264 · doi:10.5781/jwj.2019.37.6.5

Study on the Mechanism of Nugget Growth Behavior in Three Sheets Stack Resistance Spot Welding

2019· article· en· W2995663264 on OpenAlexaff
Nazmul Huda, Dae-Geun Nam, Yeong-Do Park

Bibliographic record

VenueJournal of Welding and Joining · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersDong-Eui University
KeywordsSpot weldingWeldabilityMaterials scienceWeldingComposite materialMetallurgyStack (abstract data type)Ultimate tensile strength

Abstract

fetched live from OpenAlex

The present study focused on finding a mechanism to achieve good weldability in three sheets spot welding. Two combinations of three sheets (Al-HPF combinations with mild steel / HPF steel / DP steel stacking) and (TRIP combination with mild steel / TRIP780 steel / DP590 steel stacking) were used. Weld quality was evaluated by measuring the button diameter after peel test, and peak load measurement using a tensile shear test. A high speed camera and simulation (SORPAS) were used to investigate the nugget growth mechanism. The Al-HPF combinations showed higher nugget growth rate in the sheet thickness direction, however the TRIP combination showed higher nugget growth rate in the sheet rolling direction. Also, the TRIP combination produced a larger nugget diameter (or higher penetration) in the thin sheet of interface "A" than the Al-HPF combination. The main reason for the larger nugget diameter in the TRIP combination was the higher current density and wider current path resulting from the high indentation on interface "A". The selection of a middle sheet with optimum electro-mechanical properties for the three sheet stack can improve the weldability of three sheets spot welding for car body construction.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.252
Teacher spread0.233 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

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