MétaCan
Menu
Back to cohort
Record W4309664321 · doi:10.3390/jfb13040259

Effect of Cooling and Annealing Conditions on the Microstructure, Mechanical and Superelastic Behavior of a Rotary Forged Ti–18Zr–15Nb (at. %) Bar Stock for Spinal Implants

2022· article· en· W4309664321 on OpenAlexafffund
Konstantin Lukashevich, Vadim Sheremetyev, A. A. Komissarov, В. В. Чеверикин, В. А. Андреев, С. Д. Прокошкин, Vladimir Braïlovski

Bibliographic record

VenueJournal of Functional Biomaterials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureAnnealing (glass)MetallurgyComposite materialBar (unit)

Abstract

fetched live from OpenAlex

In this work, the microstructure, phase state, texture, superelastic and mechanical properties of a Ti–18Zr–15Nb (at. %) shape memory alloy subjected to a combined thermomechanical treatment, including hot rotary forging with either air cooling or water quenching and post-deformation annealing are studied. It was revealed that the main structural component of the deformed and annealed alloy is BCC β-phase. With an increase in the forging temperature from 600 to 700 °C, the average grain size increases from 5.4 to 17.8 µm for the air-cooled specimens and from 3.4 to 14.7 µm for the water-quenched specimens. Annealing at 525 °C after forging at 700 °C with water quenching leads to the formation of a mixed statically and dynamically polygonized substructure of β-phase. In this state, the alloy demonstrates the best combination of functional properties in this study: a Young’s modulus of ~33 GPa, an ultimate tensile strength of ~600 MPa and a superelastic recovery strain of ~3.4%.

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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Functional BiomaterialsSame topicTitanium Alloys Microstructure and PropertiesFrench-language works237,207