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Constitutive characterization of an 1800 MPa press hardening steel under hot stamping conditions

2021· article· en· W3174919472 on OpenAlexaff
Song Lu, Sante DiCecco, Michael J. Worswick, C. I. CHIRIAC, George Luckey, Jimi Tjong, J. C. Boettger, Chengcheng Shi

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsFord Motor Company (Canada)University of Waterloo
Fundersnot available
KeywordsHot stampingMaterials scienceConstitutive equationDigital image correlationIsothermal processViscoplasticityUltimate tensile strengthTensile testingHardening (computing)Strain hardening exponentComposite materialFinite element methodStampingMetallurgyStructural engineeringThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract High temperature isothermal tensile tests were performed on an aluminized 1800 MPa press hardening steel with a Gleeble thermal-mechanical test system. A procedure was developed to enable the use of digital image correlation (DIC) analysis of the tensile specimens. Flow curves ranging from 500°C - 900°C with strain rates ranging from 0.01 s −1 – 1 s −1 were obtained. To validate this DIC procedure, flow curves for the more common PHS1500 (22MnB5) steel were also obtained in an identical manner and compared to results found in the existing literature. The resultant flow curves were then post-processed and fit to a thermal-viscoplastic constitutive equation for numerical simulation implementation. Finally, a finite element model of the tensile test was created to evaluate the accuracy of the constitutive results.

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 score0.016
Threshold uncertainty score0.789

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 communication0.0000.002
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 head0.021
GPT teacher head0.225
Teacher spread0.204 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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