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Characterizing the Constitutive Properties of AA7075 for Hot Forming

2017· article· en· W2758063512 on OpenAlexafffund
Kaab Omer, S Kim, C. Butcher, Michael J. Worswick

Bibliographic record

VenueJournal of Physics Conference Series · 2017
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsHonda Development and Manufacturing of America
KeywordsNeckingMaterials scienceHot stampingConstitutive equationQuenching (fluorescence)Ultimate tensile strengthHardening (computing)Strain rateComposite materialTensile testingStress (linguistics)Die (integrated circuit)MetallurgyFinite element methodStructural engineering

Abstract

fetched live from OpenAlex

The work presented herein investigates the constitutive properties of AA7075 as it undergoes a hot stamping/die quenching process. Tensile specimens were solutionized inside a heated furnace set to 470°C. Once solutionized, the samples were quenched to an intermediate temperature using a vortex air chiller at a minimum rate of 52°C/s. Tensile tests were conducted at steady state temperatures of 470, 400, 300, 200, 115 and 25°C. This solutionizing and subsequent quenching process replicated the temperature cycle and quench rates representative of a die quenching operation. The results of the tensile test were analyzed with digital imaging correlation using an area reduction approach. The area reduction approach approximated the cross-sectional area of the tensile specimen as it necked. The approach allowed for the true stress-strain response to be calculated well past the initial necking point. The resulting true stress-strain curves showed that the AA7075 samples experienced almost no hardening at 470°C. As steady state temperature decreased, the rate of hardening as well as overall material strength increased. The true stress strain curves were fit to a modified version of the extended Voce constitutive model. The resulting fits can be used in a finite element model to predict the behaviour of an AA7075 blank during a die quenching operation.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.273
Teacher spread0.209 · 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".

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Citations12
Published2017
Admission routes2
Has abstractyes

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