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Constants for hot deformation constitutive models for recent experimental data

2010· article· en· W2132149106 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience and Technology of Welding & Joining · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaComisión Nacional de Investigación Científica y TecnológicaColorado School of MinesNational Science Foundation
KeywordsMaterials scienceForgingWeldingMetalworkingMetallurgyConstitutive equationFriction stir weldingCreepTitanium alloyDeformation (meteorology)AlloyComposite materialStructural engineeringFinite element methodEngineering

Abstract

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This paper presents previously unavailable constants for the Sellars and Tegart constitutive model for hot metalworking. The materials considered are aluminium alloys 2024, 5083, 6061, 7050, 7075 and 356, carbon steel 1018, stainless steel 304, titanium alloy 6Al–4V, and magnesium alloys AZ31 and AZ61. These materials and their mechanical properties at high temperature are of great interest for latest generation manufacturing processes involving deformation to accomplish solid state joining, such as friction stir welding, cold spray and magnetic impulse welding. The results are also useful to model established processes, such as hot rolling, forging and creep. The methodology used to obtain the constants consists on non-linear regressions based on partial data sets as it was conducted previously. The input data were obtained from published values for hot compression experiments. All regressions presented here have a coefficient of determination R 2 >0·95. When possible, the results obtained were compared to previous published regressions.

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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: none
Teacher disagreement score0.353
Threshold uncertainty score0.310

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 studies0.0000.001
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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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