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Record W2895579157 · doi:10.1080/02670836.2018.1525860

A novel ultra-strong hot stamping steel treated by quenching and partitioning process

2018· article· en· W2895579157 on OpenAlexaff
Jiangtao Liang, Zhengzhi Zhao, Binhan Sun, Hongzhou Lu, Juhua Liang, He Qin, Weijian Chen, Tang Di

Bibliographic record

VenueMaterials Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsHot stampingMaterials scienceElongationDuctility (Earth science)MicrostructureQuenching (fluorescence)AusteniteToughnessMetallurgyStampingComposite materialUltimate tensile strength

Abstract

fetched live from OpenAlex

In the present study, we design a novel hot stamping steel containing high amounts of C and Si and micro-alloying element (i.e. Nb). The steel was subjected to quenching and partitioning (Q&P) process. The new Q&P treated hot stamping steel exhibits a significantly improved mechanical property in terms of strength, ductility and impact toughness compared with the traditional 22MnB5 hot stamping steel. The influence of partitioning time on the microstructure and mechanical properties was investigated. The retained austenite (RA) fraction and the carbon content of RA significantly increased with higher partitioning times. With increasing partitioning time, the uniform elongation, total elongation, strength-ductility balance and impact energy was also remarkably enhanced. The maximum strength-ductility balance achieves around 23 GPa %.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.216
Teacher spread0.208 · 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 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

Citations25
Published2018
Admission routes1
Has abstractyes

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