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Record W2094526600 · doi:10.3139/146.111252

Microstructural investigation on marforming and conventional cold deformation in Ni–Ti–Fe-based shape memory alloys

2015· article· en· W2094526600 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.

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2015
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)MetallurgyMicrostructureLiquid nitrogenAlloyDislocationGrain sizeDeformation (meteorology)Composite material

Abstract

fetched live from OpenAlex

Abstract A hot-rolled Ni–Ti–Fe alloy was subjected to 50% cold rolling by laboratory rolling mill and was subsequently annealed at 800°C for 1.5 h. This sample was then deformed through another 10% reduction in thickness by two different routes (i) conventional cold rolling and (ii) marforming (rolling in liquid nitrogen) followed by annealing under identical conditions. The grain refinement during normal cold rolling was attributed to relatively large presence of dislocations in the ND // <110> grains in the starting microstructure. The regions of higher dislocation densities became gradually textured to ND // <111> orientation, with cold rolling. Marforming (deformation in liquid nitrogen following phase transformation) on the other hand led to more significant grain refinement and also change in the bulk texture. The objective of this study was to compare the grain refinement and microstructural modification produced through marforming with that obtained in conventional cold 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.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.347
Teacher spread0.257 · 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