MétaCan
Menu
Back to cohort

Influence of Starting Microstructure on Dilatation Behavior during Tempering of a High Strength Steel

2018· article· en· W2906079599 on OpenAlexaff
Seyyed Hesamodin Talebi, Hadi Ghasemi‐Nanesa, Mohammad Jahazi, Haikouhi Melkonyan

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTemperingBainiteMartensiteMaterials scienceMicrostructureAusteniteMetallurgyCarbideComposite material

Abstract

fetched live from OpenAlex

The aim of this study was to determine the effect of non-isothermal tempering on microstructure evolution in large-size slabs. Using high-resolution dilatometry, three different cooling rates (from 0.08 to 3°C/s) representative of different regions from the surface to the core of the slab were experimentally simulated, and then tempering was carried out for each starting microstructure. A combination of light and electron microscopy and X-ray diffraction analyses were employed to accurately analyze different phenomena occurring during the tempering process, specially, the identification of different microstructures (bainite, martensite and retained austenite), and the determination of the percentage of retained austenite for each experimental condition were considered. Experimental results revealed that the microstructure after the cooling rate of 0.08°C/s consisted of bainite and some retained austenite. For the cooling rate of 0.3°C/s, martensite plus bainite was detected, and when the cooling rate was increased to 3°C/s, a martensitic microstructure was obtained. Analysis of dilatometry curves indicated that tempering behavior varied significantly with the starting microstructure. Martensite tempering was accompanied with a length decrease due to the decomposition of medium-carbon martensite to low-carbon martensite plus carbides. Tempering of bainite and retained austenite resulted in a remarkable length increase.

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 score0.021
Threshold uncertainty score0.481

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)0.0000.000

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207