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Die Quench Process Sensitivity of AA7050

2019· article· en· W2990847342 on OpenAlexaff
R. Boulis, Sante DiCecco, 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
KeywordsQuenching (fluorescence)Materials scienceUltimate tensile strengthIsothermal processElongationMetallurgyAlloyComposite materialAluminiumHeat transferDie (integrated circuit)Sensitivity (control systems)Thermodynamics

Abstract

fetched live from OpenAlex

Abstract The current work investigates the sensitivity of AA7050 aluminum alloy sheet to the thermal cycle involved in the die quenching (DQ) process. This entailed the variation of three key parameters in the thermal cycle, namely solutionizing time, transfer time and quench rate, considering both water quenching (WQ) and die quenching (DQ) at various die pressures. Following a lab-grade T6 heat treatment, Vickers hardness (HV-1000) measurements of all test conditions and tensile testing on a reduced number of test conditions were completed. The results showed only limited sensitivity to solutionizing time for the range of conditions tested. Longer transfer times and slower cooling rates both negatively affected final hardness properties, with up to 4% reduction in final hardness relative to the water-quenched T6 condition. Higher cooling rates during die-quenching produced statistically similar final tensile properties to those achieved from water quenching, following a T6 heat-treatment; while slower cooling rates resulted in significant reductions in tensile strength and uniform elongation. Limiting dome height testing of a 101.6 mm dome sample under die-quench conditions produced a major true limit strain of 0.42. The die-quench limit strain compared favourably against the limit strains of the same geometry in the T6 condition at room temperature and 150 °C (isothermal), which were 0.12 and 0.18, respectively.

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 score0.004
Threshold uncertainty score0.711

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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Same venueIOP Conference Series Materials Science and EngineeringSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207