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Record W2886936201 · doi:10.1002/srin.201800207

Role of Prior Martensite in a 2.0 GPa Multiple Phase Steel

2018· article· en· W2886936201 on OpenAlexaff
Xixi Cui, Derek O. Northwood, Cheng Liu

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Windsor
FundersYangzhou University
KeywordsMartensiteAusteniteMaterials scienceIsothermal processQuenching (fluorescence)Ferrite (magnet)BainiteUltimate tensile strengthMicrostructureMetallurgyPhase (matter)Isothermal transformation diagramComposite materialThermodynamics

Abstract

fetched live from OpenAlex

A 55Mn2SiCr steel is developed by a novel multiple‐step process, which involves austenitizing at 900 °C for 30 min, rapid quenching to 210 °C, then holding at 170 °C for 5 min, and isothermally holding at 250 °C for different times, and finally cooling in air. The mixed microstructure consists of lenticular prior martensite (PM), fine needle bainitic ferrite (BF), and filmy retained austenite (RA). The results show that the highest tensile strength of 2030 MPa with a bending strength of 4000 MPa is achieved at 250 °C for 120 min. This is attributed to a synergistic multi‐phase strengthening effect. The presence of martensite formed during the quenching process prior to the isothermal treatment, accelerates the kinetics of subsequent nano‐scaled super bainitic transformation by bainitic laths nucleating quickly at the martensite‐austenite interfaces. The product of austenite fraction and its carbon content is found to be another important factor for controlling the strength. In addition, the phase evolution as well as carbon partitioning mechanism during isothermal treatment is discussed.

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 score0.292
Threshold uncertainty score0.750

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 communication0.0000.000
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 head0.040
GPT teacher head0.338
Teacher spread0.298 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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