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Mechanocyclic and Time Stability of the Loading-Unloading Diagram Parameters of Nanostructured Ti-Nb-Ta and Ti-Nb-Zr SMA

2013· article· en· W1967905337 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials science forum · 2013
Typearticle
Languageen
FieldMaterials Science
TopicTitanium Alloys Microstructure and Properties
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceModulusAnnealing (glass)Shape-memory alloyUltimate tensile strengthComposite materialMetallurgyDeformation (meteorology)

Abstract

fetched live from OpenAlex

The Ti-21.8Nb-6Zr and Ti-19.7Nb-5.8Ta (at.%) shape memory alloys are thermomechanically treated by cold drawing and post-deformation annealing at 550-600°C forming a nanosubgrained structure in the β-phase. Cyclic mechanical testing using a “loading-unloading” mode with 2% tensile strain in each half-cycle reveals the non-perfect superelastic behavior of both alloys during the very first cycles of testing, which becomes perfect during further mechanocycling. The Young’s modulus of thermomechanically-treated alloys is low (about 45 GPa), and it decreases during mechanocycling (n=10 cycles) down to 25-35 GPa, approaching the Young’s modulus of cortical bone tissues. The Young’s modulus obtained in the 10th cycle is stable or changes only slightly during a further 40-day pause at room temperature and then during repeated mechanocycling. The residual strain per cycle, the transformation yield stress and the mechanical hysteresis decrease during mechanocycling. Subsequent to a 40-day pause at room temperature, they restore their initial values. Repeated mechanocycling is accompanied by a repeated decrease of these parameters.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.198
Teacher spread0.189 · 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