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Mechanical Behavior of High-Strength Low-Alloy Steels

2018· book· en· W4230400976 on OpenAlexaff
Ricardo Branco, Filippo Berto

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMcGill University
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVAgentúra na Podporu Výskumu a Vývoja
KeywordsAlloyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Bainitic steels play an important role in the modern automotive and rail industries because of their balanced properties. Understanding the relationship between the bainitic microstructure features and the fatigue performance is a fundamental ingredient in developing safer and durable products. However, so far this relationship is not sufficiently clear. Therefore, there is the need to strengthen the knowledge within this field. The present paper aims at comparing the uniaxial cyclic behaviour of carbide-bearing and carbide-free bainitic steels. To meet this goal, fully-reversed strain-controlled tests at various strain amplitudes were performed. After the final failure, fracture surfaces were observed by transmission electron microscopy to relate the bainitic morphology to the fatigue performance. The main findings of this work show that the carbide-free lower bainite has superior fatigue performance compared to the carbide-bearing lower bainite. This is explained by the presence of stable carbides and thick bainite ferrite plates.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.631
Threshold uncertainty score1.000

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)0.0070.000

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

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