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3D Meso-Scale Modeling of Solidification for Aluminum-Alloy Welding

2014· article· en· W1973258182 on OpenAlexaff
H.R. Zareie Rajani, A.B. Phillion

Bibliographic record

VenueMaterials science forum · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceWeldingMesoscale meteorologyWeld poolAlloyMicrostructureScale (ratio)MetallurgyAluminiumMechanicsArc weldingGas tungsten arc welding

Abstract

fetched live from OpenAlex

Meso-scale modeling through the use of a granular-type model is a key new tool for predicting solidification-related defects. In the present study, the application of a granular-type model to welding microstructure is presented, along with application challenges and solutions. This new model can simulate the solidification of a weld pool at the mesoscale, i.e. both solid grains and liquid are included. Consequently, the behaviour of the semisolid structure within the weld pool can be studied. By means of this 3D meso-scale model, the continuous network of liquid channels that forms at the last stages of solidification have been investigated, allowing for prediction of the variation in the distribution of liquid channel width as a function of welding parameters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.512

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 head0.012
GPT teacher head0.217
Teacher spread0.206 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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