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Substructure and Nanocrystalline Structure Effects in Thermomechanically Treated Ti-Ni Alloys

2006· article· en· W2075994322 on OpenAlexaff
К. Inaekyan, С. Д. Прокошкин, Vladimir Braïlovski, I. Yu. Khmelevskaya, Vincent Demers, С. В. Добаткин, E. V. Tat’yanin, E. Bastarache

Bibliographic record

VenueMaterials science forum · 2006
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
FundersMinistry of Education and Science of the Russian FederationLady Tata Memorial Trust
KeywordsSubstructureMaterials scienceNanocrystalline materialAusteniteMetallurgyAnnealing (glass)Differential scanning calorimetryTransmission electron microscopyAlloyAtmospheric temperature rangeThermomechanical processingDislocationDeformation (meteorology)Composite materialCrystallographyMicrostructureThermodynamicsStructural engineeringNanotechnology

Abstract

fetched live from OpenAlex

Substructure and structure formation as well as functional properties of thermomechanically treated Ti-Ni wire have been studied using differential scanning calorimetry, X-ray diffraction, transmission electron microscopy and mechanical. The low- temperature themomechanical treatment (LTMT) was carried out by rolling at room temperature in a true strain range e = 0.3 to 1.9. It was shown that severe plastic deformation (e=1.9) of Ti-50.0at.%Ni alloy results in partial amorphization and formation of nanocrystalline austenite structure during post-deformation annealings up to 400 °C. As a result, the fully recoverable strain and recovery stress become much higher than the values reachable after traditional LTMT (e=0.3 to 0.88) with post-deformation annealing which creates a poligonized dislocation substructure.

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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