MétaCan
Menu
Back to cohort
Record W4375842873 · doi:10.3901/jme.2022.20.350

Study on Dynamic Tensile Mechanical Behavior and Deformation Mechanisms of CrCoNi Medium Entropy Alloy at Room and Cryogenic Temperature

2022· article· en· W4375842873 on OpenAlexaff
Chang Hui, Tuanwei Zhang, Li Zhiqiang

Bibliographic record

VenueJournal of Mechanical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsImpact
Fundersnot available
KeywordsMaterials scienceAlloyUltimate tensile strengthCryogenic temperatureDeformation (meteorology)Composite materialMetallurgy

Abstract

fetched live from OpenAlex

摘要: CrCoNi中熵合金在准静态拉伸下具有良好的强度和塑性,而其动态拉伸力学行为还有待研究。利用霍普金森拉杆分别对CrCoNi中熵合金试样进行了室温(298 K)和低温(77 K)下不同应变率的动态拉伸力学行为研究,建立了修正的J-C(Johson-Cook)本构模型对其塑性流动行为进行了较好的描述,通过断后样品的微观组织表征揭示了其变形机理。结果表明:室温下CrCoNi中熵合金的强度和塑性随着应变率增大逐渐提高。与准静态拉伸相比,动态拉伸应变率为1 200~5 000 s-1时,试样的屈服强度增大至560 MPa到1 150 MPa,伸长率增长至60%到90%;低温下强度表现出相似的应变率效应且强度较室温下更高,但韧性有所降低。变形机理结果表明:相比于室温准静态,室温动态拉伸下试样内部孪晶密度更大且交叉孪晶出现、FCC→HCP相变发生、纳米晶形成,三者共同作用促使CrCoNi中熵合金加工硬化提高;相比于室温动态拉伸,低温动态拉伸下试样孪晶密度过大导致孪晶增厚,且纳米晶形成,促使试样加工硬化进一步提高,而孪晶增厚加强了对位错的阻碍致使韧性降低。

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 score0.004
Threshold uncertainty score0.010

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Mechanical EngineeringSame topicHigh Entropy Alloys StudiesFrench-language works237,207