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Record W7132099560

AA7075高强铝合金热冲压流变行为本构模型对比研究

2020· article· en· W7132099560 on OpenAlexvenueno aff
Ning Wang, Andrey Ilinich, Minghe Chen, George Lucky, Guillaume D’Amours

Bibliographic record

VenueNPARC · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsHot stampingFlow stressConstitutive equationStrain rateBlankDeformation (meteorology)Tensile testingUltimate tensile strengthAlloy
DOInot available

Abstract

fetched live from OpenAlex

In hot stamping, the high strength aluminum alloy AA7075 blank was first fully solutionized and then transferred into room temperature tools, stamped and quenched. To characterize the AA7075 alloy hot deformation behavior, tensile tests employing the heating path representative of the hot stamping process were performed over the temperature range of 200-480 °C and strain rate range of 0.01-10 s-1. Modified constitutive models based on the Arrhenius type model, Johnson-Cook model and Zerilli-Armstrong model were proposed and calibrated with the hot tensile test data. The proposed models coupled the strain, strain rate and temperature effects on flow stress by expressing the model parameters as polynomial functions of strain, strain rate and temperature. The prediction accuracy of the constitutive models on flow stress was evaluated by the mean square error (MSE) and the correlation coefficient R value. The results indicated that the modified Johnson-Cook model can provide most accurate prediction for the AA7075 hot flow behavior.

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)0.000
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0000.000
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 head0.015
GPT teacher head0.185
Teacher spread0.170 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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