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Record W4308300556 · doi:10.54691/sjt.v4i10.2369

Simplified Methodology Research for Parameter Setting of Resistance Spot Welding

2022· article· en· W4308300556 on OpenAlexaff
Xuefeng Wang

Bibliographic record

VenueScientific Journal of Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsWeldingSpot weldingMechanical engineeringSample (material)Computer scienceEngineeringReliability engineering

Abstract

fetched live from OpenAlex

The research is explored an efficient methodology in parameter setting for RSW (resistance spot welding). The methodology mainly focusses on weld cycles and weld current, which has included a process for parameter selection, performance evaluation, and failure mode analysis. Effects on welding resistance caused by various technical parameters. Relationship between weld cycles, weld current and the mechanical performance will be established by simplified model. Also, the parameter setting of welding operation, inspection methods, failure modes and control system will be discussed in the following content. This methodology will lead to higher efficiency and optimized quality during for manufacture parameter determination in sample A round before massive production. A proper resistance spot welding will be more economical engineering method for joints in the industry.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.345
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.110
GPT teacher head0.393
Teacher spread0.282 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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