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Record W2771438397 · doi:10.2495/cmem-v6-n3-455-462

Effect of prior martensite on mechanical properties of austempered ductile iron

2017· article· en· W2771438397 on OpenAlexaff
Chen Yang, Derek O. Northwood, Cheng Liu

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Windsor
FundersYangzhou University
KeywordsAustemperingMartensiteMetallurgyMaterials scienceDuctile ironCast ironComposite materialBainiteMicrostructure

Abstract

fetched live from OpenAlex

An unalloyed ductile iron, which incorporates C and Si as major and Mn as minor alloying elements, is processed by a novel austempering process, in order to obtain superior mechanical properties. The samples are initially austenitized at 890C for 20 min, then quenched into patented water-based quenching liquid at 180C for 0.5, 2 and 3.5 s respectively, and austempered at 220C for 240 min in an electric furnace. Optical microscopy (OM) and scanning electron microscopy (SEM) are performed to correlate the mechanical properties with microstructural characteristics. It is found that partial martensite can be formed firstly upon quenching, which will accelerate the subsequent bainitic transformation and promote refinement of multiphase colonies during austempering. The prior martensite content increases with increasing holding time during quenching. A tensile strength of 1330MPa, an elongation of 3.13% and a hardness of 45HRC can be achieved by controlling the prior martensite content to 12%. SEM of fracture surfaces reveals a mixed ductile and cleavage rupture morphology type in all samples. The results indicate that the tensile behavior of the investigated ADI is mainly influenced by the content of prior martensite.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.414

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 head0.048
GPT teacher head0.357
Teacher spread0.309 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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Same venueInternational Journal of Computational Methods and Experimental MeasurementsSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207