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Record W2088486741 · doi:10.1115/1.4027492

Metallurgical Investigations on Hyperplasticity in Dual Phase Steel Sheets

2014· article· en· W2088486741 on OpenAlexafffund
Javad Samei, Daniel E. Green, Sergey F. Golovashchenko

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsFormabilityMaterials scienceCrystal twinningFerrite (magnet)Dual-phase steelMartensiteMetallurgyNucleationDeformation (meteorology)Composite materialMicrostructure

Abstract

fetched live from OpenAlex

Several researchers have reported that dual phase steel sheets exhibit hyperplasticity, that is, a significant formability improvement under certain high strain rate forming conditions. Hyperplastic behavior of dual phase steels formed using an electrohydraulic forming (EHF) process was previously investigated by the authors at both macro- (Golovashchenko et al., 2013, “Formability of Dual Phase Steels in Electrohydraulic Forming,” J. Mater. Process. Technol., 213, pp. 1191–1212) and microscales (Samei et al., 2013, “Quantitative Microstructural Analysis of Formability Enhancement in Dual Phase Steels Subject to Electrohydraulic Forming,” J. Mater. Eng. Perform., 22(7), pp. 2080–2088). A relative deformation improvement of approximately 20% in ferrite grains and 100% in martensite islands was reported in the EHF specimens compared to specimens formed under quasi-static conditions. In this paper, the remarkable deformation improvements of the constituents are discussed in terms of metallurgical mechanisms of deformation. The nucleation and multiplication of dislocations in ferrite and deformation twinning in martensite were found to be the principal mechanisms responsible for the significant improvements of deformation in EHF. In addition, these mechanisms enhance the plastic compatibility between the two phases which reduces the risk of decohesion and delays the onset of fracture in EHF specimens.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.213
Teacher spread0.201 · 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

Citations11
Published2014
Admission routes2
Has abstractyes

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