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Record W4400949852 · doi:10.3390/ma17153645

Multiple Deformation Mechanisms in Adiabatic Shear Bands of a Titanium Alloy during High Strain Rate Deformation

2024· article· en· W4400949852 on OpenAlexafffund
X.R. Guan, Dong-Rong Liu, Shoujiang Qu, Guojian Cao, Hao Wang, Aihan Feng, D.L. Chen

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsToronto Metropolitan University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAdiabatic shear bandMaterials scienceCrystal twinningMartensiteDiffusionless transformationShear (geology)Strain rateComposite materialDeformation (meteorology)InstabilityShear bandMetallurgyDislocationTitanium alloyDeformation mechanismAlloyTitaniumMicrostructureMechanics

Abstract

fetched live from OpenAlex

The occurrence of adiabatic shear bands, as an instability phenomenon, is viewed as a precursor to failure caused by instability at high strain rates. Metastable β titanium alloys are extensively utilized due to their excellent mechanical properties, which are often subjected to high strain rate loads in service conditions. Understanding and studying their adiabatic shear instability behavior is thus crucial for preventing catastrophic failure and enhancing material performance. In this study via detailed microstructural analyses in the adiabatic shear region of a Ti-10V-2Fe-3Al alloy subjected to high strain rates, it was observed that α″ martensitic transformation and nano-twinning plus β-to-α phase transformation with α″ martensite as an intermediate phase occurred, in addition to substantial fine grains. The grain refinement mechanisms were mainly related to dynamic recovery dominated by dislocation migration alongside severe plastic deformation.

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 score0.002
Threshold uncertainty score0.004

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 head0.207
Teacher spread0.198 · 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

Citations12
Published2024
Admission routes2
Has abstractyes

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