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Record W3136047507 · doi:10.18280/acsm.450104

Analysis of the Influence of Hot Impacts on the Transformation of White Cast Iron

2021· article· en· W3136047507 on OpenAlexvenueno aff
Ekbal Mohammed Saeed Salih, Ahmed O. Al-Roubaiy, Yasser Louy Azeez Salih

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsCementiteCast ironMetallurgyMaterials sciencePeriod (music)Transformation (genetics)White (mutation)ChemistryMicrostructureAustenite

Abstract

fetched live from OpenAlex

Heat treatments are the most common method of transforming or modifying the structure of white cast iron. Cementite tapes can be decomposed at high temperatures and over a long period of time. These thermal treatments require special furnaces and a long period of time, as well as a high cost with major problems associated with these techniques. In this study, a mechanical thermal treatment was employed, which includes two basic stages, the first being heating at a certain temperature 1100℃ for a rather short period of time, then applying sequential strokes (one stroke or group of strokes) for a period not exceeding a few minutes. The findings proved that heating for short or long periods of time and at 1100℃ is not sufficient to get rid of cementite tapes, as the structure remained white cast iron. The important matter here is that the effect of the hot impact on the transformation of white cast iron into grey is related to the formation ratio. In this regard, the results uncover that using hot impact and at the same temperature, but at higher rates of forming (i.e., greater than 70%) the structure is completely transformed into grey cast iron.

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 score0.284
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.269
Teacher spread0.227 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicMetal Alloys Wear and PropertiesFrench-language works237,207