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Record W4411346062 · doi:10.4028/p-wm5xmn

Effect of Cooling Rate on the As-Cast Microstructure of a High Strength Steel Produced in a Large Size Ingot

2025· article· en· W4411346062 on OpenAlexaff
Abdelhalim Loucif, D. Shahriari, Kanwal Chadha, Chun Ping Zhang, Jean-Benoı̂t Morin, Mohammad Jahazi

Bibliographic record

VenueKey engineering materials · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of New BrunswickÉcole de Technologie Supérieure
Fundersnot available
KeywordsIngotMicrostructureMaterials scienceMetallurgyAlloy

Abstract

fetched live from OpenAlex

This paper presents an experimental study on the influence of solidification cooling rate on the evolutions of microstructural morphologies of a high strength low alloy steel. To this end, solidification samples (cylindrical form with 10 mm diameter and 120 mm length) were prepared from 30 cm below the ingot/hot-top interface, at the center, of a 40 MT (Metric Ton) ingot. Solidification experiments were carried out by using Gleeble ® 3800 thermo-mechanical simulator. Two solidification cooling rates of 1 and 50°C/s were chosen. For microstructural characterization, samples were prepared by mounting, polishing and etching with 3% Nital solution. Also, an optical microscope was employed for microstructural observations. The obtained results showed that for 1°C/s, the microstructure is composed with dendrites and grains. Here, the grain morphology is the dominant one. In the case of 50°C/s, the dendrites were localized at the sample surface and the grains were present more into the depth of the sample. Moreover, the increase of solidification cooling rate results in finer dendrites. The results are discussed in the framework of solidification mechanisms.

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)0.001
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 score0.013
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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