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Record W3132246930 · doi:10.22215/etd/2020-14243

Phase Field Modelling of Bainitic Transformation

2020· dissertation· en· W3132246930 on OpenAlexaff
F. Elhigazi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsCarleton University
Fundersnot available
KeywordsNucleationMaterials scienceFerrite (magnet)Phase field modelsCarbideAusteniteSupersaturationBainiteMetallurgyMicrostructureBeta ferritePhase (matter)ThermodynamicsComposite materialChemistry

Abstract

fetched live from OpenAlex

A phase field model was developed to study the interaction between the displacive transformation, diffusion process and carbide formation during the bainitic type transformation.The thermodynamic data for the chemical free energy were obtained using the Thermo-Calc software.Also, the transformation strains reproducing the γ → α transformation with three transformation variants of ferrite were used.In the case of carbide free bainite, the bainitic transformation develops in two stages: the first, fast stage, wherein the displacive transformation is dominant at the onset of the transformation, and the second stage, which begins when the diffusion-controlled decomposition takes control over the transformation kinetics and ferrite morphology, and results in the formation of a carbon enriched layer around ferrite grains.Both plate-like and rod-like shapes of the ferrite grains can be obtained depending on the thermodynamic conditions and diffusion mobility.The carbide nucleation was modeled as the formation of carbon sinks either in retained austenite or in supersaturated ferrite with different nucleation sequences.The results indicated that in the case of carbide formation in retained austenite, the carbide nucleation might play a secondary role controlling the transformation kinetics and microstructure evolution after the completion of the fast transformation stage; however, elastic interactions could control the ferrite morphology even at a later stage, thus leading to the change of the ferrite grain shape from a rod-like to plate-like.In the case of carbide formation in supersaturated ferrite, the results demonstrated that initially, due to a slow Preface As defined by section 12.4 of 2020-2021 Carleton University Graduate Calendar, the current thesis is an integrated Ph.D thesis.It includes the following articles which are either published (paper 1) or are under review ( papers 2,3): 1. F. Elhigazi, A. Artemev, The interaction between the displacive transformation and the diffusion process in the bainitic type transformation, Computational Materials Science.169 (2019) 109079.2. F. Elhigazi, A. Artemev, The interaction between the carbon partitioning and carbide nucleation inside austenite during the bainitic type transformation, Computational Materials Science.3. F. Elhigazi, A. Artemev, The influence of carbide formation in ferrite on the bainitic type transformation, Computational Materials Science.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.235
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2020
Admission routes1
Has abstractyes

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