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Record W2021940169 · doi:10.1115/1.4023725

Optimizing Temper Bead Welding by Computational Weld Mechanics and Design of Experiment Matrix

2013· article· en· W2021940169 on OpenAlexaff
Mahyar Asadi, Christopher Bayley, John Goldak

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsCarleton University
Fundersnot available
KeywordsWeldingTransient (computer programming)BeadMaterials scienceMatrix (chemical analysis)Mechanical engineeringMechanicsMetallurgyComputer scienceComposite materialEngineeringPhysics

Abstract

fetched live from OpenAlex

Temper bead welding is usually done by experiment, i.e., trial and error. This paper describes a computational weld mechanics model to compute the transient temperature and transient microstructure evolution in temper bead welds. The computed hardness from this model, is compared to measured hardness for validation. Furthermore, the effects of power per unit length, welding current, and welding speed on final hardness, are studied by designing and implementing three design-of-experiment matrices.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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 designSimulation or modeling
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

Citations7
Published2013
Admission routes1
Has abstractyes

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