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Constitutive modelling of Usibor® 1500 sheets after intercritical quenching

2021· article· en· W3173638932 on OpenAlexaff
Mohammad Shirinzadeh Dastgiri, Ravi A. Thakkar, Shi Jun, Iman Sari Sarraf, D. E. Green

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceQuenching (fluorescence)Ultimate tensile strengthVolume fractionStrain rateHardening (computing)Strain hardening exponentDeformation (meteorology)Constitutive equationComposite materialMetallurgyThermodynamicsFinite element methodOptics

Abstract

fetched live from OpenAlex

Abstract In this study, 0.9 and 1.8 mm thick Usibor® 1500 sheets were subjected to intercritical quenching by heating to 760-930°C and quenching at a controlled rate. The tensile behavior of as-quenched Usibor® 1500 was experimentally obtained using uniaxial tension tests at strain rates ranging from 0.001 to 0.25 s −1 . The constants in the hardening models, including Johnson-Cook, were optimized using a Genetic algorithm and linear regression for each condition at each strain rate. Then, these models were numerically modified to account for heat treatment dependency. Uniaxial tensile tests were simulated using the fitted models and compared to experimental flow curves and strain distribution maps to determine the accuracy of the prediction of each model. Optical microscopy was used to determine the volume fraction of each phase using image processing tools and these characteristics were used to explain the behavior of Usibor® 1500 after intercritical quenching. It was found that intercritical quenching process parameters determine the distribution and morphology of each phase and consequently the range of mechanical properties. This model can be used to simulate the deformation of hot-stamped components with tailored properties produced under controlled austenitization.

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 score0.022
Threshold uncertainty score0.691

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 head0.017
GPT teacher head0.197
Teacher spread0.180 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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