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Characterization of Heat Transfer Coefficient of Lightweight Alloys in Kirksite Dies

2019· article· en· W2990354253 on OpenAlexaff
Kaab Omer, C. Butcher, Michael J. Worswick

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFormabilityMaterials scienceQuenching (fluorescence)Die (integrated circuit)AlloyHeat transfer coefficientHeat transferMetallurgyBlankAluminiumFinite element methodComposite materialMechanicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract The heat transfer coefficient (HTC) is an important parameter in the finite element (FE) modelling of warm and hot forming operations. The HTC, among other parameters, governs the FE model predictions for the cooling rate within the blank and the resulting constitutive behaviour and formability. In the current work, the HTC of two aluminum alloys (AA5182-O and AA7075-T6) and one magnesium alloy (ZEK100) is characterized. Blanks were heated in a convection furnace and subsequently quenched in a set of kirksite dies under various contact pressures. Kirksite is a zinc-based alloy commonly used in prototype tooling. The temperature-time (T-t) profile of the blanks and die were measured during each quenching experiment. The resulting T-t profiles were input into a Matlab script, which calculated the HTC using an iterative regression technique.

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)0.000
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 score0.015
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.171
Teacher spread0.164 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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